XSEDE: The Extreme Science and Engineering Discovery Environment
First Interim Project Report (IPR 1): September 1 - October 31, 2016
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XSEDE Senior Management Team (SMT) John Towns (NCSA) PI and Project Director Kelly Gaither (TACC) Co-PI and Campus Engagement and Enrichment Director Ralph Roskies (PSC) Co-PI and Extended Collaborative Support Service Co-Director Nancy Wilkins-Diehr Co-PI and Extended Collaborative Support Service Co-Director (SDSC) Greg Peterson (UT-NICS) XSEDE Operations Director David Hart (NCAR) Resource Allocations Service Director David Likfa (Cornell) XSEDE Community Infrastructure Director Ron Payne (NCSA) Program Office Director Karla Gendler (TACC) Senior Project Manager Emre Brooks (UT-San User Advisory Committee Chair (voting member of the SMT) Antonio) Dan Stanzione (TACC) XD Service Providers Forum Chair (voting member of the SMT)
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Table of Contents XSEDE: The Extreme Science and Engineering Discovery Environment ...... i XSEDE Senior Management Team (SMT) ...... ii List of Tables...... vi Reading this Report ...... viii 1. Executive Summary ...... 1 1.1. Strategic Goals ...... 1 1.2. Summary & Project Highlights ...... 2 2. Science and Engineering Highlights ...... 5 2.1. Promising Drug Leads Identified to Combat Heart Disease (Yinglong Miao, UC San Diego)5 2.2. Tornadogenesis (Amy McGovern, University of Oklahoma) ...... 6 2.3. Implications of Behind-the Meter Battery Energy Storage for U.S. Electric Grid Operations (Jay Apt, Carnegie Mellon University) ...... 7 3. Discussion of Strategic Goals and Key Performance Indicators ...... 8 3.1. Deepen and Extend Use ...... 8 3.1.1. Deepening Use to Existing Communities ...... 8 3.1.2. Extending Use to New Communities ...... 9 3.1.3. Prepare the Current and Next Generation ...... 9 3.1.4. Raising Awareness ...... 10 3.2. Advance the Ecosystem ...... 11 3.2.1. Create an Open and Evolving e-Infrastructure ...... 11 3.2.2. Enhance the Array of Technical Expertise and Support Services ...... 12 3.3. Sustain the Ecosystem ...... 12 3.3.1. Provide Reliable, Efficient, and Secure Infrastructure ...... 13 3.3.2. Provide Excellent User Support ...... 13 3.3.3. Effective and Productive Virtual Organization ...... 14 3.3.4. Innovative Virtual Organization ...... 14 4. Community Engagement & Enrichment (WBS 2.1) ...... 16 4.1. CEE Director’s Office (WBS 2.1.1) ...... 18 4.2. Workforce Development (WBS 2.1.2) ...... 18 4.3. User Engagement (WBS 2.1.3) ...... 21 4.4. Broadening Participation (WBS 2.1.4) ...... 22 4.5. User Interfaces & Online Information (WBS 2.1.5) ...... 24 4.6. Campus Engagement (WBS 2.1.6) ...... 26 5. Extended Collaborative Support Services (WBS 2.2) ...... 28
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5.1. ECSS Director’s Office (WBS 2.2.1) ...... 30 5.2. Extended Support for Research Teams (WBS 2.2.2) ...... 30 5.3. Novel and Innovative Projects (WBS 2.2.3) ...... 31 5.4. Extended Support for Community Codes (WBS 2.2.4) ...... 33 5.5. Extended Support for Science Gateways (WBS 2.2.5) ...... 34 5.6. Extended Support for Education, Outreach, & Training (WBS 2.2.6) ...... 35 6. XSEDE Community Infrastructure (WBS 2.3) ...... 37 6.1. XCI Director’s Office (WBS 2.3.1)...... 38 6.2. Requirements Analysis & Capability Delivery (WBS 2.3.2) ...... 39 6.3. XSEDE Capability & Resource Integration (WBS 2.3.3) ...... 40 7. XSEDE Operations (WBS 2.4) ...... 43 7.1. Operations Director’s Office (WBS 2.4.1) ...... 44 7.2. Cybersecurity (WBS 2.4.2) ...... 44 7.3. Data Transfer Services (WBS 2.4.3) ...... 45 7.4. XSEDE Operations Center (WBS 2.4.4) ...... 45 7.5. System Operations Support (WBS 2.4.5) ...... 46 8. Resource Allocation Service (WBS 2.5) ...... 47 8.1. RAS Director’s Office (WBS 2.5.1) ...... 48 8.2. XSEDE Allocations Process & Policies (WBS 2.5.2) ...... 48 8.3. Allocations, Accounting, & Account Management CI (WBS 2.5.3) ...... 49 9. Program Office (WBS 2.6) ...... 51 9.1. Project Office (WBS 2.6.1) ...... 53 9.2. External Relations (WBS 2.6.2) ...... 53 9.3. Project Management, Reporting, & Risk Management (WBS 2.6.3)...... 54 9.4. Business Operations (WBS 2.6.4) ...... 55 9.5. Strategic Planning, Policy & Evaluation (WBS 2.6.5) ...... 56 10. Appendices ...... 58 10.1. Glossary and List of Acronyms ...... 58 10.2. Metrics ...... 61 10.2.1. SP Resource and Service Usage Metrics ...... 61 10.2.2. Other Metrics ...... 73 10.3. Scientific Impact Metrics (SIM) and Publications Listing ...... 83 10.3.1. Summary Impact Metrics ...... 83 10.3.2. Historical Trend ...... 83 10.3.3. Publications Listing ...... 84
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11. Service Provider Forum Report ...... 162 12. TAS Summary ...... 165 12.1. Executive Summary ...... 165 12.2. XMS Findings: ...... 165 12.3. XMS Recommendations: ...... 166
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List of Tables Table 1-1: Summary of key performance indicators (KPIs) for XSEDE ...... 3 Table 3-1: KPIs for the sub-goal of deepen use to (existing communities) ...... 8 Table 3-2: KPIs for the sub-goal of extend use (new communities) ...... 9 Table 3-3: KPIs for the sub-goal of preparing the current and next generation ...... 10 Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research services ...... 10 Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure ...... 11 Table 3-6: KPIs for the sub-goal of enhance the array of technical expertise and support services ...... 12 Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure ...... 13 Table 3-8: KPIs for the sub-goal of provide excellent user support ...... 13 Table 3-9: KPIs for the sub-goal of operate an effective and productive virtual organization ...... 14 Table 3-10: KPIs for the sub-goal of operate an innovative organization ...... 15 Table 4-1: Area Metrics for Community Engagement & Enrichment ...... 16 Table 4-2: Area Metrics for Workforce Development ...... 19 Table 4-3: Area Metrics for User Engagement ...... 22 Table 4-4: Area Metrics for Broadening Participation ...... 23 Table 4-5: Area Metrics for User Interfaces & Online Information ...... 25 Table 4-6: Area Metrics for Campus Engagement ...... 26 Table 5-1: Area Metrics for Extended Collaborative Support Services ...... 28 Table 5-2: Area Metrics for Extended Support for Research Teams ...... 31 Table 5-3: Area Metrics for Novel and Innovative Projects ...... 32 Table 5-4: Area Metrics for Extended Support for Community Codes ...... 33 Table 5-5: Area Metrics for Extended Support for Science Gateways ...... 34 Table 5-6: Area Metrics for Extended Support for Education, Outreach, & Training ...... 35 Table 6-1: Area Metrics for XSEDE Community Infrastructure (XCI) ...... 37 Table 6-2: Area Metrics for Requirements Analysis & Capability Delivery ...... 39 Table 6-3: Area Metrics for XSEDE Capability & Resource Integration ...... 41 Table 7-1: Area Metrics for Operations ...... 43 Table 7-2: Area Metrics for Cybersecurity ...... 44 Table 7-3: Area Metrics for Data Transfer Services ...... 45 Table 7-4: Area Metrics for XSEDE Operations Center ...... 45 Table 7-5: Area Metrics for System Operations Support ...... 46 Table 8-1: Area Metrics for Resource Allocation Service ...... 47 Table 8-2: Area Metrics for XSEDE Allocations Process & Policies ...... 48
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Table 8-3: Area Metrics for Allocations, Accounting, & Account Management CI ...... 49 Table 9-1: Area Metrics for Program Office ...... 51 Table 9-2: Area Metrics for External Relations ...... 54 Table 9-3: Area Metrics for Project Management, Reporting, & Risk Management ...... 55 Table 9-4: Area Metrics for Business Operations ...... 55 Table 9-5: Area Metrics Strategic Planning, Policy & Evaluation ...... 57
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Reading this Report This report is the result of an ongoing process of improving reporting on the progress in delivering on our mission and realizing our goals. For a large, complex, highly-distributed project such as XSEDE, this is a considerable undertaking. This process has helped XSEDE improve as an organization and as a provider of services to the compute- and data-enabled science and engineering research and education community. XSEDE reports on its activities and progress by using a metrics-based approach. Based on feedback from our review panels we have redefined KPIs to be metrics that measure progress toward the high-level strategic goals. The key concept is not that the metrics [KPIs] themselves have a direct causal effect on eventual outcomes, but rather that the metrics are chosen so that actions and decisions which move the metrics in the desired direction also move the organization in the direction of the desired outcomes and goals. All other metrics will be termed Area Metrics. This is intended to focus the attention of external stakeholders on what we believe to be the best (key) indicators of progress toward our long- term strategic goals. The Executive Summary (§1) is intended to effectively and concisely communicate the status of the project toward delivery of the mission and realization of the vision by reaching three strategic goals. Stoplight indicators (§0) are used to visually provide a quick understanding of our assessment of overall project progress with respect to the strategic goals in light of our KPIs. The Science and Engineering Highlights (§2) provide a small selection of a continuing series of scientific and engineering research and education successes XSEDE has enabled. These successes are an ongoing testament to the importance of our services to the research community. Section three (§3), a discussion of goals and KPIs, provides the next level of detail in understanding project progress. It decomposes the strategic goals into sub-goals and discusses progress toward each of the sub-goals using KPIs that represent measures of impact to the scientific community where possible. As noted in the report, in some cases metrics for impact, outcome, or both are not currently being collected and in these cases the best available metric is used as the KPI. These first three sections take a project-wide view. A more detailed analysis of progress from the view of the areas responsible for supporting each of the sub-goals—and contributing toward the KPIs associated with those sub-goals—is provided by looking at each of the areas of the project in the remaining sections (§4, §5, §6, §7, §8, and §9). These sections also contain area highlights and additional Area Metrics that the area’s staff has deemed important. It is anticipated that this report is read in electronic form (PDF) using Adobe Reader®. There is extensive cross-linking to facilitate referencing content across the document. In general, all text that has blue underlining (e.g. §2) is clickable. Clicking on the underlined text will take you to the referenced section. These are set up to facilitate moving back and forth between the high level discussions in §1 and §3, to more detailed discussions regarding specific project areas in §4, §5, §6, §7, §8, and §9. As noted, this represents an ongoing effort at improvement and we welcome comments on how to improve any and all aspects of our reporting process.
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1. Executive Summary Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Researchers continue to integrate an increasingly diverse set of distributed resources and instruments directly into their research and educational pursuits. Access to an array of integrated and well- supported high-end digital services is critical for the advancement of knowledge. XSEDE (the Extreme Science and Engineering Discovery Environment) is a socio-technical platform that integrates and coordinates advanced digital services within the national ecosystem to support contemporary science. This ecosystem involves a highly distributed, yet integrated and coordinated, assemblage of software, supercomputers, visualization systems, storage systems, networks, portals and gateways, collections of data, instruments and personnel with specific expertise. XSEDE fulfills the need for an advanced digital services ecosystem distributed beyond the scope of a single institution and provides a long-term platform to empower modern science and engineering research and education. As a significant contributor to this ecosystem, driven by the needs of the open research community, XSEDE substantially enhances the productivity of a growing community of scholars, researchers, and engineers. XSEDE federates with other high- end facilities and campus-based resources, serving as the foundation for a national e-science infrastructure with tremendous potential for enabling new advancements in research and education. Our vision is a world of digitally-enabled scholars, researchers, and engineers participating in multidisciplinary collaborations while seamlessly accessing computing resources and sharing data to tackle society’s grand challenges. Researchers use advanced digital resources and services every day to expand their understanding of our world. More pointedly, research now requires more than just supercomputers, and XSEDE represents a step toward a more comprehensive and cohesive set of advanced digital services through our mission: to substantially enhance the productivity of a growing community of scholars, researchers, and engineers through access to advanced digital services that support open research; and to coordinate and add significant value to the leading cyberinfrastructure resources funded by the NSF and other agencies. XSEDE has developed its strategic goals in a manner consistent with NSF’s strategies stated broadly in the Cyberinfrastructure Framework for 21st Century Science and Engineering1 vision document, and the more specifically relevant Advanced Computing Infrastructure: Vision and Strategic Plan2 document. 1.1. Strategic Goals To support our mission and to guide the project’s activities toward the realization of our vision, three strategic goals are defined: Deepen and Extend Use: XSEDE will deepen the use—make more effective use—of the advanced digital services ecosystem by existing scholars, researchers, and engineers, and extend the use to new communities. We will contribute to preparation—workforce development—of the current and next generation of scholars, researchers, and engineers in the use of advanced digital services via training, education, and outreach; and we will raise the general awareness of the value of advanced digital services.
1 http://www.nsf.gov/cise/aci/cif21/CIF21Vision2012current.pdf 2 http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf12051
PY6 IPR 1 Page 1 Advance the Ecosystem: Exploiting its internal efforts and drawing on those of others, XSEDE will advance the broader ecosystem of advanced digital services by creating an open and evolving e-infrastructure, and by enhancing the array of technical expertise and support services offered. Sustain the Ecosystem: XSEDE will sustain the advanced digital services ecosystem by ensuring and maintaining a reliable, efficient, and secure infrastructure, and providing excellent user support services. XSEDE will further operate an effective, productive, and innovative virtual organization. The strategic goals of XSEDE cover a considerable scope. To assure we are delivering our mission and to assess progress toward our vision, we have identified key metrics to measure our progress toward meeting each sub-goal. These key performance indicators (KPIs) are a high- level encapsulation of our project metrics that measure how well we are meeting each sub-goal. Planning is driven by our vision, mission, goals, and these metrics— which are in turn rooted in the needs and requirements of the communities we serve. The key concept is not that the KPIs themselves must have a direct causal effect on eventual outcomes, or measure eventual outcomes or long-term impacts, but rather that the KPIs are chosen so that actions and decisions which move the metrics in the desired direction also move the organization in the direction of the desired outcomes and goals. Table 3-1 below shows the project’s progress toward the three strategic goals and associated sub-goals. Status icons are used in the table as follows: A green status is defined as a strategic goal for which at least 90% of the targets for all KPIs are met. A yellow status is defined as a strategic goal within which at least 60% of the targets for all KPIs are met. A red status is a strategic goal with less than 60% of the KPI targets met.
A white status indicates there are currently no metrics tracked for this sub-goal or there is not complete data for any of the metrics tracked. Multiple indicators represent a strategic goal that has sub-goals for which there is incomplete data or that have metrics not currently tracked. In these cases, the second indicator is a qualitative assessment of the status provided in lieu of sufficient data or a formal metric being in place. 1.2. Summary & Project Highlights As reflected in Table 3-1, XSEDE2 is off to a very good start. Significant improvements were made during the transition from XSEDE1 to XSEDE2. Two items of note during the initial two months of PY6 come from the Community Engagement & Enrichment (CEE), and XSEDE Community Infrastructure (XCI) teams. CEE has implemented a new strategy for engaging underrepresented students by way of our inaugural challenge for advanced computing for social change taking place at SC16. This challenge resulted organically from the student program at XSEDE16. Details of this event can be found in §4. XCI delivered the new Community Software Repository (XCSR) to enable the broader XSEDE community to share and discover software information, and to facilitate integration of new software into the XSEDE ecosystem. See §6 for additional information.
PY6 IPR 1 Page 2 Table 1-1: Summary of key performance indicators (KPIs) for XSEDE
Strategic Goals Sub-goals KPIs
Deepen and Extend Use (§3.1)
● Number of completed ECSS projects Deepen use (existing ● Average ECSS impact rating communities) (§3.1.1) ● Average satisfaction with ECSS support
● Number of new users from underrepresented communities and non- Extend use (new traditional disciplines of XSEDE resources and services communities) (§3.1.2) ● Number of sustained users from underrepresented communities and non-traditional disciplines of XSEDE resources and services
● Number of attendees in synchronous and asynchronous training Prepare the current and ● Average impact assessment of training for attendees registered next generation (§3.1.3) through the XSEDE User Portal
● Number of pageviews to the XSEDE website Raise awareness of the ● Number of pageviews to the XSEDE User Portal value of advanced digital ● Number of social media impressions services (§3.1.4) ● Number of media hits
Advance the Ecosystem (§3.2)
Create an open and ● Number of new capabilities made available for production evolving e-infrastructure deployment (§3.2.1) ● Average satisfaction rating of XCI services
Enhance the array of ● Average rating of staff regarding how well-prepared they feel to technical expertise and perform their jobs support services (§3.2.2)
Sustain the Ecosystem (§3.3)
Provide reliable, ● Average composite availability of core services efficient, and secure ● Hours of downtime with direct user impacts from an XSEDE security infrastructure (§3.3.1) incident
● Mean time to ticket resolution Provide excellent user ● Average user satisfaction rating for allocations and other support support (§3.3.2) services
Operate an effective and productive virtual ● Percentage of recommendations addressed by relevant project areas organization (§3.3.3)
Operate an innovative ● Number of strategic or innovative improvements virtual organization ● Ratio of proactive to reactive improvements (§3.3.4) ● Number of staff publications
As summarized in §2 of this report, XSEDE resources have been used in some impressive research projects. This includes a unique approach in identifying promising drug leads that may
PY6 IPR 1 Page 3 selectively combat heart disease, and a way of using computational thinking to help understand and solve the scientific problems surrounding the formation of tornadoes. Another project highlights the implications of behind-the-meter battery energy storage for U.S. electric grid operations.
PY6 IPR 1 Page 4 2. Science and Engineering Highlights 2.1. Promising Drug Leads Identified to Combat Heart Disease (Yinglong Miao, UC San Diego) Using a unique computational approach to rapidly sample, in millisecond time intervals, proteins in their natural state of gyrating, bobbing, and weaving, a research team from UC San Diego and Monash University in Australia has identified promising drug leads that may selectively combat heart disease, from arrhythmias to cardiac failure. Reported in the September 5, 2016 Proceedings of the National Academy of Sciences (PNAS) Early Edition, the researchers used the computing power of Gordon and Comet, based at the San Diego Supercomputer Center (SDSC) at UC San Diego; and Stampede, at the Texas Advanced Computing Center at the University of Texas at Austin, to perform an unprecedented survey of protein structures using accelerated molecular dynamics or aMD—a method that performs a more Figure 1: The M2 muscarinic complete sampling of the myriad shapes and acetylcholine receptor (orange ribbons) conformations that a target protein molecule may go plays a key role in regulating the human through. The simulations were performed using NAMD heart rate and heart contraction forces. on Gordon and Comet at SDSC, which showed excellent In contrast to compounds that bind to the highly conserved "orthosteric" scalability with hundreds of CPUs, while the team binding site and cause serious side performed some urgent accelerated molecular effects (yellow spheres), allosteric dynamics simulations on Stampede at TACC owing to modulators (purple spheres) bind to its speed. target sites with great sequence diversity and provide potential Though effective in most cases, today’s heart selective treatment of heart disease. medications—many of which act on M2 muscarinic Credit: Yinglong Miao, Howard Hughes acetylcholine receptors or M2 mAChRs that decrease Medical Institute at UC San Diego heart rate and reduce heart contractions—may carry side effects, sometimes serious. That’s because the genetic sequence of M2 mAChR’s primary ‘orthosteric’ binding site is “highly conserved,” and found in at least four other receptor types that are widely spread in the body, yielding unwanted results. For this reason, drug designers are seeking a different approach, homing in on molecular targets or so-called “allosteric binding sites” that reside away from the receptor’s primary binding site and are built around a more diverse genetic sequence and structure than their counterpart ‘orthosteric’ binding sites. Essentially, allosteric modulators act as a kind of cellular dimmer- switch that, once turned on, ‘fine tunes’ the activation and pharmacological profile of the target receptor. In particular, drug designers have begun to aggressively search for allosteric modulators to fine- tune medications that bind to G protein-coupled receptors (GPCRs), the largest and most diverse group of membrane receptors in animals, plants, fungi and protozoa. These cell surface receptors act like an inbox for messages in the form of light energy, hormones and neurotransmitters, and perform an incredible array of functions in the human body.
PY6 IPR 1 Page 5 In fact, between one-third to one-half of all marketed drugs act by binding to GPCRs, treating diseases including cancer, asthma, schizophrenia, Alzheimer’s and Parkinson’s disease, and heart disease. More Targeted Therapies Though many of the GPCR drugs have made their way to the medicine cabinet, most, including M2 mAChR targeted drugs, exhibit side effects owing to their lack of specificity. All these drugs target the orthosteric binding sites of receptors, thus creating the push to find more targeted therapies based on allosteric sites. Enter accelerated molecular dynamics and supercomputing. As described in this latest study, called accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor, some 38 lead compounds were selected from a database of compounds from the National Cancer Institute, using computationally enhanced simulations to account for binding strength and receptor flexibility. About half of these compounds exhibited the hallmarks of an allosteric behavior in subsequent in vitro experiments, with about a dozen showing strong affinity to the M2 mAChR binding site. Of these, the researchers highlighted two showing both strong affinity and high selectivity in studies of cellular behavior. These cutting- edge experiments were performed by collaborators from the Monash Institute of Pharmaceutical Sciences. “To our knowledge, this study demonstrates for the first time an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies,” the researchers wrote. 2.2. Tornadogenesis (Amy McGovern, University of Oklahoma) A team based at the University of Oklahoma is using computational thinking to help understand and solve the scientific problems surrounding the formation of tornadoes by trying to identify precursors of tornadoes in supercell simulations by generating high resolution simulations of these thunderstorms using TACC's Stampede supercomputer. In addition to high resolution simulations, the team is also using a combination of data mining and visualization techniques as they explore the factors that separate tornado formation from tornado failure. The team has been working with ECSS scientific visualization expert Greg Foss at the Texas Advanced Computing Center (TACC). Serving as a bridge between science research and the lay person, Foss says he enjoys working through XSEDE and highlighting the value and validity of the program. "I believe in our mission and I believe in the visualization field. It's quite a sense of accomplishment to help our users and even be a part of the science." For this project, Foss says that he's learned more about all of the aspects of weather than any of his six past weather projects.
PI Amy McGovern offered a few thoughts about the Figure 2: Negative gradient threshold importance of XSEDE and her work with Foss: “XSEDE is (blue) depicts primary (big part) and fabulous. We’ve been using XSEDE resources for years. I low-level (lower tube part) updrafts. started out with resources at my university and then Strong vorticity (red) is wrapping into the primary updraft. The scientists quickly outgrew what they had. They pointed me to hypothesize the evolution of this XSEDE. I started out at NICS using Darter, and when that gradient differs between tornadic and went away, I started using Stampede at TACC. These tornado failure storms.
PY6 IPR 1 Page 6 resources are fundamental … you can’t do this kind of data mining on your PC. Greg (Foss) comes at the problem from a completely different perspective, and provides new ways of looking at the data that you wouldn’t have thought of in the beginning. Once you get into a domain, it’s easy to think, ‘This is the only way to look at it,’ but then someone else comes along and asks, ‘Why are you doing it like that?’” 2.3. Implications of Behind-the Meter Battery Energy Storage for U.S. Electric Grid Operations (Jay Apt, Carnegie Mellon University) State and federal policies encourage adoption of stationary battery storage to help integrate renewable generation and provide valuable services to the electric grid. Storage is being installed both on the utility side of the customer meter at the transmission/distribution level (“grid-scale”), and at individual building sites, which is referred to as “behind the meter” (BTM). The literature has focused mostly storage by electricity providers but not industrial or private BTM users, with both the economic incentive structure and the net carbon emissions of BTM batteries largely unknown. Michael Fisher, working in the Jay Apt group at Carnegie Mellon University, used PSC’s interim Greenfield system/DXC and, more recently, XSEDE resource Bridges at PSC, to create an optimization model to investigate how a fleet of BTM batteries would behave using metered load data from over 600 commercial and industrial buildings. The Greenfield work was instrumental in adapting the group’s previous, personal-computer-based model to the Unix/HPC environment. They cite XSEDE ECSS expert Roberto Gomez of PSC as instrumental in optimizing the group’s MATLAB-based workflow in the multi-core environment, a necessity for running parallel Figure 3: Net CO2 emission rates assuming the system can predict usage computations on many buildings in order to bring the perfectly (colored bars), compared with runtime down and allow testing of different settings of results for an imperfect forecast based the simulation parameters. The run on Greenfield on historical averaging (gray bars). allowed the group to identify the parameters most Results are for industrial customers likely to be of interest and needing further (left) wholesale power providers (“wholesale-only,” right) and investigation. This in turn allowed the group to run a “aggregators,” or third parties that sensitivity analysis on Bridges that showed positive net supply battery storage. Negative net emission rates are driven mostly by internal energy emissions for California utilities in the losses and not the timing of charging/discharging. The wholesale-only perspective are skewed by extremely low utilization rates (low work is reflected in a paper now in submission. delivered energy). Results are from group’s subsequent use of PSC’s Bridges system.
PY6 IPR 1 Page 7 3. Discussion of Strategic Goals and Key Performance Indicators The strategic goals of XSEDE (§1.1) cover a considerable scope. Additionally, the specific activities within our scope are often very detailed; therefore, to ensure that this significant and detailed scope will ultimately deliver our mission and realize our vision, we decompose the three strategic goals into components or sub-goals to be considered individually. In determining the best measures of progress toward each of the sub-goals, KPIs that correlate to impact on the scientific community are used. These often pair measurements of outcome with an assessment of quality or impact to provide both a sense of scope and significance of the supporting activities. In some cases, metrics for impact, outcome, or both are not currently being collected. In these cases, the best available metric is used. 3.1. Deepen and Extend Use XSEDE will 1) deepen the use—make more effective use—of the advanced digital services ecosystem by existing scholars, researchers, and engineers and 2) extend the use to new communities. We will 3) contribute to preparation—workforce development—of scholars, researchers, and engineers in the use of advanced digital technologies via training, education, and outreach; and we will 4) raise the general awareness of the value of advanced digital research services. 3.1.1. Deepening Use to Existing Communities Although efforts to identify new technologies and new service providers along with efforts to evolve the e-infrastructure and enhance the research prowess of current and future researchers all serve to also deepen use, the collaborative, yearlong work done to help research teams more effectively and broadly use the ecosystem is the best indicator of deeper use. These efforts enable increased scale and efficiency of usage and allow use of new capabilities for the delivery of science. The project has chosen three metrics (Table 3-1) that together measure the scope, quality, and impact of these activities: 1) the total number of projects completed by the Extended Collaborative Support Services team through work with research teams, community codes, and gateways and the average ratings for 2) satisfaction with and 3) overall impact from the ECSS work. Satisfaction and overall impact are scores provided by the PIs of the projects on a 1-5 scale following project completion.
Table 3-1: KPIs for the sub-goal of deepen use to (existing communities)
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of PY10 ECSS (§5) completed ECSS PY9 projects PY8 PY7 PY6 50/yr 10 Average ECSS PY10 ECSS (§5) impact rating PY9 PY8 PY7 4 of 5 4.56 PY6 /qtr Average PY10 ECSS (§5) satisfaction PY9
PY6 IPR 1 Page 8 Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year with ECSS PY8 support PY7 4.5 of 5 PY6 4.86 /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Overall we are on-track to meet or exceed our targets. 3.1.2. Extending Use to New Communities New communities are defined as new fields of science, industry, and under-represented communities. New fields of science are those that represent less than 1% of XSEDE Resource Allocation Committee (XRAC) allocations. The Novel & Innovative Projects (NIP) team and the Broadening Participation team both work to bring advanced digital services to new communities. XSEDE measures both the number of new users and the number of sustained users on research projects from under-represented communities and non-traditional disciplines of XSEDE resources and services as the indicators of progress (Table 3-2).
Table 3-2: KPIs for the sub-goal of extend use (new communities)
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of new ECSS — NIP PY10 users from (§5.3) underrepresented PY9 communities and CEE — non-traditional Broadening disciplines of PY8 Participation XSEDE resources (§4.4) and services PY7
PY6 >200/qtr 297 Number of ECSS — NIP PY10 sustained users (§5.3) from CEE — PY9 underrepresented Broadening communities and Participation PY8 non-traditional (§4.4) disciplines of PY7 XSEDE resources and services PY6 >1,100/qtr 773 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We are on track to significantly exceed the targets tentatively set for PY6, These KPIs were redefined for XSEDE2. If these trends hold up over the course of PY6, we will increase the targets for PY7 accordingly. 3.1.3. Prepare the Current and Next Generation Part of XSEDE’s mission is to provide a broad community of existing and future researchers with access and training to use advanced digital services via the sub-goal of preparing the current and next generation of computationally-savvy researchers. While many activities support this sub- goal, such as the various Champion (§4.6), Student Engagement (§4.4), and Education (§4.2) programs, the training offered through Community Engagement & Enrichment (CEE) impacts
PY6 IPR 1 Page 9 the most people directly. This, and a complementary measure of impact as indicated by those same individuals, are therefore considered the key indicators (Table 3-3) of performance toward this goal.
Table 3-3: KPIs for the sub-goal of preparing the current and next generation
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of PY10 CEE — attendees in PY9 Workforce synchronous Development and PY8 (§4.2) asynchronous PY7 training PY6 5,600/yr 1,304 Average impact PY10 CEE — assessment of Workforce training for PY9 Development attendees (§4.2) PY8 registered through the PY7 XSEDE User Portal PY6 4 of 5/qtr 4.54 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. The training area participant numbers for September-October were low for the quarter as they were tallied by hand. The numbers reported include only registered, verified, attendees who could be identified by XSEDE User Portal (XUP) name; the actual number of attendees is higher than shown here. We now have tools to enter the data into the database and are working on entering backlog data. This more accurate data will be provided for all future reports and data for this period will be updated.. 3.1.4. Raising Awareness While many PY6 activities, such as our Workforce Development (§4.2), User Engagement (§4.3) and Broadening Participation (§4.4) efforts, and the visibility of our Champions and other Campus Engagement (§4.6) activities, contribute to our efforts to measure our ability to raise the general awareness of the value of advanced digital research services, we have chosen to focus on outcomes in four areas (Table 3-4): website, social media, public relations, and media hits. Desirable trends in these key outcomes can be correlated to success for this sub-goal.
Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research services
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of PY10 Community pageviews to Engagement the XSEDE PY9 and website Enrichment— PY8 UII (§4.5) PY7 80,000 PY6 49,409 /qtr Number of PY10 Community pageviews to Engagement PY9
PY6 IPR 1 Page 10 Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year the XSEDE PY8 and Enrichment User Portal — UII (§4.5) PY7
100,000 PY6 183,408 /qtr
Number of PY10 Program Office Social Media — ER (§9.2) impressions PY9 PY8 PY7 190,000 PY6 52,500 /yr Number of PY10 Program Office media hits — ER (§9.2) PY9 PY8 PY7 PY6 140/yr 32 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We continue to meet our public facing metrics through the website and social media, and are doing extremely well with respect to funneling users and potential users through the XSEDE User Portal. As we approach the end of PY6, we will reevaluate our targets to ensure that we encourage growth and impact. 3.2. Advance the Ecosystem Exploiting its internal efforts and drawing on those of others, XSEDE will advance the broader ecosystem of advanced digital services by 1) creating an open and evolving e-infrastructure, and by 2) enhancing the array of technical expertise and support services offered. 3.2.1. Create an Open and Evolving e-Infrastructure External factors, such as the number of XSEDE Federation members and the variety of services they provide alongside internal efforts, such as Operations (§7) of critical infrastructure and services and the evaluation and integration of new capabilities, all affect the evolution of the e- infrastructure. While we actively seek new Federation members and Service Providers, and partnerships with national and international cyberinfrastructure projects, we view our role as connector of these elements to be the most impactful. Thus, XSEDE focuses on the number of new capabilities made available for production deployment along with the satisfaction rating of all XSEDE Community Infrastructure (XCI) services as indicators of performance with respect to this sub-goal (Table 3-5).
Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of PY10 XCI (§6) new PY9
PY6 IPR 1 Page 11 Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year capabilities PY8 made PY7 available for production PY6 7/yr 0 deployment Average PY10 XCI (§6) satisfaction PY9 rating of XCI services PY8 PY7 4 of 5 4.8 PY6 /yr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. The KPI for “Number of new capabilities made available for production deployment” is below its target value. This is due to the fact that we have not fully delivered any new XSEDE2 Use Cases because we have been finishing up XSEDE1 deliverables. The KPI for “average satisfaction rating of XCI services” meets its target value. 3.2.2. Enhance the Array of Technical Expertise and Support Services To enhance the technical expertise of our staff to offer an evolving set of support services, we will continue many activities including workshops, symposia, and training events hosted by Extended Collaborative Support Services (ECSS) and Service Providers (§5.6). The average rating by staff for training will continue to be a KPI; the staff climate survey question that measures this has been changed to focus on staff having the training they need, instead of staff turning to XSEDE training to fulfill these needs (Table 3-6). This represents a change in approach to staff training that reflects budget constraints, where we will leverage existing training offered by the Service Providers, universities, and professional associations alongside our training to enhance the expertise of staff.
Table 3-6: KPIs for the sub-goal of enhance the array of technical expertise and support services
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Average rating PY10 Program Office of staff PY9 — Strategy regarding how Planning, Policy well-prepared PY8 & Evaluation they feel to PY7 (§9.5) perform their 4 of 5 PY6 3.70 jobs /yr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. In 2015 the average rating of staff regarding how well-prepared they feel to perform their jobs was 3.66, this has improved to 3.70 in 2016. 3.3. Sustain the Ecosystem XSEDE will sustain the advanced digital services ecosystem by 1) ensuring and maintaining a reliable, efficient, and secure infrastructure, and 2) providing excellent user support services. Furthermore, XSEDE will operate an 3) effective, 4) productive, and 5) innovative virtual organization.
PY6 IPR 1 Page 12 3.3.1. Provide Reliable, Efficient, and Secure Infrastructure Many activities support this sub-goal—such as User Information & Interfaces (§4.5), Security (§7.2), Data Transfer Services (§7.3), Systems Operations and Support (§7.5), support for Allocations (§8.2), and Allocations, Accounting & Account Management (§8.3)—but perhaps the truest measure of an infrastructure’s reliability is its robustness as reflected by sustained availability. Thus, the KPI for this sub-goal is a composite measure of the availability of XSEDE infrastructure components and the number of hours of downtime with direct user impacts from a security incident (Table 3-7). The composite measure is a geometric mean of the availability of critical enterprise services and the XRAS allocations request management service.
Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Average PY10 XSEDE composite PY9 Operations (§7) availability of RAS (§8) core services PY8 PY7 PY6 99%/qtr 99.9% Hours of PY10 Cybersecurity downtime with PY9 (§7.2) direct user impacts from PY8 an XSEDE PY7 security PY6 < 24/qtr 0 incident NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. KPIs for this area have met or exceeded targets. 3.3.2. Provide Excellent User Support Although nearly every group in the organization has some support function, we have chosen to focus on metrics with respect to two primary support interfaces to the community: the XSEDE Operations Center (XOC) metrics with respect to ticket resolutions times and the average satisfaction rating of the allocations process. The XOC is the frontline centralized support group that either resolves or escalates tickets to the appropriate resolution center depending on the request. Two KPIs are measured (Table 3-8): the mean time to resolution on support tickets that are resolved by the XOC or routed to, and resolved by, other XSEDE areas, and the average satisfaction rating for the allocations process measured via a quarterly survey of users who have interacted with the allocations request system and the allocations process more generally.
Table 3-8: KPIs for the sub-goal of provide excellent user support
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Mean time to PY10 XSEDE ticket Operations (§7) PY9 resolution (hours) PY8 PY7 PY6 < 24/qtr 24.0 PY10 RAS (§8)
PY6 IPR 1 Page 13 Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Average user PY9 satisfaction PY8 with allocations PY7 process and other support PY6 4 of 5/yr 3.98 services NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Please see RAS (§8) for detailed explanation of below target number for Q1 for user satisfaction. 3.3.3. Effective and Productive Virtual Organization During the first five years of XSEDE, in conjunction with developing a methodology for driving and assessing performance excellence, XSEDE adopted the Baldrige Criteria3 and has assessed and applied applicable criteria from all seven criteria by that methodology. These include annual reviews of the vision, mission, strategic goals, project-wide processes and standards (KPIs and area metrics); user and staff surveys (§4.3, §9.5); stakeholder communications (§9.2); advisory boards (§9.1); community engagement (§4); workforce enhancement (§4.2); and the analysis of organizational data that leads to organizational learning, strategic improvement and innovation. Thus, XSEDE has chosen to monitor its continuous improvement efforts as an organizational measurement of recommendations addressed by the relevant project areas (Table 3-9). The idea here is that a stable or expanding number of improvements shows that XSEDE continues to systematically evaluate the organization and make informed, proactive improvements.
Table 3-9: KPIs for the sub-goal of operate an effective and productive virtual organization
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Percentage of PY10 Project Office recommendations PY9 — Strategic addressed by Planning, relevant project PY8 Policy & areas PY7 Evaluation PY6 90%/qtr NA1 (§9.5) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 L2 Directors are currently responding to climate study recommendations; data will be available in Q2. As we are only two months into PY6, the recommendations for the most recent Climate Survey are being reviewed and considered across all Level 2 and Level 3 areas. Future IPRs will contain updates to this KPI 3.3.4. Innovative Virtual Organization Measuring innovation for an organization like XSEDE (or for organizations in general) is difficult and represents an area of open research. A partial measure is the number of staff publications produced since this shows that XSEDE staff is involved in novel activities that achieve peer- reviewed publication. Additionally, after much thought and discussion both internally and with external stakeholders and advisors, we have identified two additional indicators that strongly correlate to innovation: 1) the ratio of proactive organizational improvements to those that were
3 http://www.nist.gov/baldrige/
PY6 IPR 1 Page 14 reactive and 2) the number of improvements that are innovative or lead to innovations. The first indicator is a measurement of organizational maturity and agility; the second measures innovative actions directly (Table 3-10). While these provide some indication of innovation, they are still not satisfactory. These KPIs will continue to be the subject of an open conversation within the organization and with stakeholders and advisors as XSEDE assesses these measurements and how to best quantify innovation. In particular, this will be discussed in earnest within the Strategic Planning, Policy and Evaluation team (§9.5).
Table 3-10: KPIs for the sub-goal of operate an innovative organization
Program KPI Target Q1 Q2 Q3 Q4 Total Owner Year Number of PY10 Project Office strategic or — Strategic PY9 innovative Planning, improvements PY8 Policy & PY7 Evaluation (§9.5) PY6 9/yr 3 Ratio of PY10 Project Office proactive to — Strategic PY9 reactive Planning, improvements PY8 Policy & PY7 Evaluation (§9.5) PY6 3:1/yr 8:1 Number of PY10 Program staff Office (§9) PY9 publications PY8 PY7 PY6 70/yr 5 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We are on-track to meet the staff publication target. The high ratio of proactive to reactive improvements is attributed to the extra effort of making improvements as part of the transition from XSEDE1 to XSEDE2.
PY6 IPR 1 Page 15
4. Community Engagement & Enrichment (WBS 2.1) At the core of Community Engagement & Enrichment (CEE) is the researcher, broadly defined to include anyone who uses or may potentially use the array of resources and services offered by XSEDE. The CEE team is dedicated to actively engaging a broad and diverse cross-section of the open science community, bringing together those interested in using, integrating with, enabling, and enhancing the national cyberinfrastructure. Vital to the CEE mission is the persistent relationship with existing and future users, including allocated users, training participants, XSEDE collaborators, and campus personnel. CEE will unify public offerings to provide a more consistent, clear, and concise message about XSEDE resources and services, and bring together those aspects of XSEDE that have as their mission teaching, informing, and engaging those interested in advanced cyberinfrastructure. The five components of CEE are Workforce Development (§4.2), which includes Training, Education and Student Preparation, User Engagement (§4.3), Broadening Participation (§4.4), User Interfaces & Online Information (§4.5) and Campus Engagement (§4.6). These five teams will ensure routine collection and reporting of XSEDE’s actions to address user requirements. They will provide a consistent suite of web-based information and documentation and engage with a broad range of campus personnel to ensure that XSEDE’s resources and services complement those offered by campuses. Additionally, CEE teams will expand workforce development efforts to enable many more researchers, faculty, staff, and students to make effective use of local, regional, and national advanced digital resources. CEE will expand efforts to broaden the diversity of the community utilizing advanced digital resources. The CEE team will tightly coordinate with the rest of XSEDE, particularly Extended Collaborative Support Services (§5), Resource Allocation Services (§8), XSEDE Community Infrastructure (§6), and External Relations (§9.2). CEE is focused on personal interactions, ensuring that existing users, potential users and the general public have sufficient access to materials and have a positive and effective experience with XSEDE public offerings and frontline user support. As such, the CEE area metrics are designed to broadly assess this performance. CEE has focused on metrics that will quantify how many users in aggregate are benefiting from XSEDE resources and services. Additionally, CEE has focused on how well the user base is sustained over time and how well training offerings evolve with changing user community needs.
Table 4-1: Area Metrics for Community Engagement & Enrichment Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of new PY10 Deepen/Extend users of XSEDE PY9 — Extend use to resources and new services PY8 communities PY7 (§3.1.1) > 1,000 PY6 1,881 /qtr Number of PY10 Deepen/Extend sustained users of PY9 — Deepen use XSEDE resources to existing and services PY8 communities PY7 (§3.1.1) > 5,000 PY6 4,755 /qtr
PY6 IPR 1 Page 16 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of new PY10 Deepen/Extend users from PY9 — Extend use to underrepresented new communities using PY8 communities XSEDE resources PY7 (§3.1.2) and services PY6 > 100/qtr 150
Number of PY10 Deepen/Extend sustained users PY9 — Deepen use from to existing underrepresented PY8 communities communities using PY7 (§3.1.1) XSEDE resources > 1,000 and services PY6 322 /yr (KPI) Number of PY10 Deepen/Extend attendees in PY9 — Prepare the synchronous and current and asynchronous PY8 next generation training PY7 (§3.1.3) > 5,000 PY6 1,304 /yr Average impact PY10 Deepen/Extend assessment of PY9 — Prepare the training for current and attendees PY8 next generation registered through PY7 (§3.1.3) XSEDE User Portal PY6 4 of 5/qtr 4.54 Number of PY10 Deepen/Extend pageviews to the PY9 — Raise XSEDE website awareness of PY8 the value of PY7 advanced digital 80,000 services (§3.1.4) PY6 49,409 /qtr Number of PY10 Deepen/Extend pageviews to the PY9 — Raise XSEDE User Portal awareness of PY8 the value of PY7 advanced digital 100,000 services (§3.1.4) PY6 183,408 /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. CEE is either on target or far exceeding our metrics. We have done extremely well reaching out to new and potential users in both mainstream and under-represented users. The Training team has done a tremendous job of examining metrics to ensure reproducibility and to ensure that we leverage our resources to maximize impact. We continue to meet and make progress towards our public facing metrics through the website and are doing extremely well with respect to funneling users and potential users through the XSEDE user portal. As we approach the end of PY6, we will reevaluate our metrics to ensure that we encourage growth and impact. We have implemented an exciting new strategy for engaging under-represented students with our inaugural challenge for advanced computing for social change taking place at SC16. This challenge resulted organically from the student program at XSEDE16, where diversity/inclusion were a major pillar of the conference. We know that our student population, by and large
PY6 IPR 1 Page 17 Millennials and Post-Millennials, are now the largest, most diverse sector of our population and represent the largest generation to date. According to a report analyzing the millennials by The Council of Economic Advisers in 2014, these two generations have grown up in an age of pervasive and often-times ubiquitous technology, violence, and with the introduction of the internet, information overload. They are more likely to be constantly connected to their smart phones and have multiple means of communicating with their peers via social media. In sharp contrast, they also express a greater value to the role they play in their communities, including a close relationship with their families and they rate quality of life as being very important as well as a strong desire to make a positive social impact on their own children and communities and society at large.4 While there are a number of initiatives that target the general issues of under-representation in STEM fields, they don’t directly address the needs of the advanced research computing community and scientists using advanced computing resources and tools to solve our nation’s grand challenges. Our focus on advanced computing for social change provides opportunities to bring together multicultural, diverse groups of students and researchers to work as a team on issues affecting social change, leveraging what those studying organizational dynamics have known for some time. A team’s ability to innovate requires the integration of different perspectives, knowledge, experiences and backgrounds, allowing us to break through creative barriers and apply resulting innovations to problems that are too large to be solved by any one team member. The inaugural challenge will take place at SC16 and will include 19 students from a diverse set of backgrounds, genders, and races. Another major success in the CEE area is the configuration and deployment of a User Requirements project in JIRA for tracking user issues. The User Interfaces & Online Information team has done an excellent job of working directly with the User Engagement team to begin to put the tracking mechanism in production. This includes a completed configuration of JIRA to gather and organize user requirements that are gathered from multiple sources – surveys, tickets, user interviews, etc. 4.1. CEE Director’s Office (WBS 2.1.1) The CEE Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the CEE area. This oversight includes providing direction to the L3 management team, coordination of, and participation in, CEE planning activities and reports through the area’s Project Manager. The Director’s Office also attends and supports the preparation of project level reviews and activities. The CEE Director’s Office will continue to manage and set direction for CEE activities and responsibilities. They will contribute to and attend bi-weekly senior management team calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF and attend XSEDE quarterly and annual meetings. 4.2. Workforce Development (WBS 2.1.2) The Workforce Development mission is to provide a continuum of learning resources and services designed to address the needs and requirements of researchers, educators, developers, integrators, and students utilizing advanced digital resources. This includes providing professional development for the XSEDE team members.
4 The Council of Economic Advisers. (2014). 15 Economic Facts About Millennials. Washington, DC: Executive Office of the President of the United States.
PY6 IPR 1 Page 18 Workforce Development provides an integrated suite of training, education, and student preparation activities to address formal and informal learning about advanced digital resources addressing the needs of researchers, developers, integrators, IT staff, XSEDE staff, faculty, and undergraduate and graduate students. CEE – Workforce Development will provide business and industry with access to XSEDE’s workforce development efforts including training services and student internships that have proven beneficial to industry in the first five years of the project. Workforce Development is comprised of three areas: Training, Education and Student Preparation. The Training team will develop and deliver training programs to enhance the skills of the national open science community and ensure productive use of XSEDE’s cyberinfrastructure. The Education team will work closely with Training and Student Preparation to support faculty in all fields of study with their incorporation of advanced digital technology capabilities within the undergraduate and graduate curriculum. The Student Preparation program will actively recruit students to use the aforementioned training and education offerings to enable the use of XSEDE resources by undergraduate and graduate students to motivate and prepare them to pursue advanced studies and careers to advance discovery and scholarly studies. The Training metrics have been dramatically revised based on lessons learned. Beginning with PY6, the metrics will include the number of people who attended training rather than the number of people who registered. This will more accurately represent the number of people who are benefitting from the training offerings. Further, since the learning environment and usage modalities for synchronous (which includes in-person and web-cast events) and asynchronous (which includes usage of CI-Tutor and Cornell Virtual Workshop tutorials) training are very different in nature, the number of people benefitting from these will be reported separately. In addition, the number of unique people benefitting from the full range of training offerings, as well as the number of people benefitting from each of the synchronous and asynchronous training offerings, will be reported. These metrics will better represent the impact of training among the community. The Education metrics are consistent with those reported in previous years. They will include the adoption and incorporation of computation and data-enabled techniques and methods within the curriculum, and the sharing of course materials. This will include offerings of certificate and degree programs, new and modified course modules, the number of modules shared with the community, and the downloads of course modules for adoption and/or adaptation in courses. The Student Preparation metrics will include the number of students engaged in utilizing XSEDE resources and services through internships, their participation in the annual XSEDE Conference, or working on XSEDE projects. Also included will be the number of under-represented students who are involved.
Table 4-2: Area Metrics for Workforce Development Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of unique PY10 Deepen/Extend — attendees, PY9 Prepare the current synchronous and next generation training PY8 (§3.1.3) PY7 1,600 PY6 112 /yr PY10
PY6 IPR 1 Page 19 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of total PY9 Deepen/Extend — attendees, PY8 Prepare the current synchronous and next generation training PY7 (§3.1.3) (One person can 2,000 PY6 188 take several /yr classes) Number of unique PY10 Deepen/Extend — attendees, PY9 Prepare the current asynchronous and next generation training PY8 (§3.1.3) PY7 2,000 PY6 215 /yr Number of total PY10 Deepen/Extend — attendees, PY9 Prepare the current asynchronous and next generation PY8 training (One (§3.1.3) person can take PY7 several classes) 4,000 PY6 1,116 /yr Average impact PY10 Deepen/Extend — assessment of PY9 Prepare the current training for and next generation attendees PY8 (§3.1.3) registered PY7 through XSEDE 4 of 5 PY6 4.54 User Portal /qtr Number of formal PY10 Deepen/Extend — degree, minor, and PY9 Prepare the current certificate and next generation programs added PY8 (§3.1.3) to the curricula PY7 PY6 3/yr 1 Number of PY10 Deepen/Extend — materials PY9 Prepare the current contributed to and next generation public repository PY8 (§3.1.3) PY7 PY6 40/yr 10 Number of PY10 Deepen/Extend — materials PY9 Prepare the current downloaded from and next generation the repository PY8 (§3.1.3) PY7 56,000 PY6 11,114 /yr Number of PY10 Deepen/Extend — computational PY9 Prepare the current science modules and next generation added to courses PY8 (§3.1.3) PY7 PY6 40/yr - Number of PY10 Deepen/Extend — students PY9 Prepare the current benefitting from PY8
PY6 IPR 1 Page 20 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported XSEDE resources PY7 and next generation and services PY6 50/qtr 276 (§3.1.3) Percentage of PY10 Deepen/Extend — under- PY9 Prepare the current represented and next generation students PY8 (§3.1.3) benefitting from PY7 XSEDE resources 50% PY6 and services /qtr 42% NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. -Data reported annually. The training area participant numbers for September-October were low for the quarter as they were tallied by hand. The numbers reported include only registered, verified, attendees who could be identified by XUP portal name; the actual number of attendees is higher than shown here. We now have tools to enter the data into the database and are working on entering backlog data and setting up database access and queries for future reports. We will update the reported number in the next IPR. The number of students benefiting from XSEDE resources exceeded the quarterly goal of 50 students by 226 students for a total of 276 students. This is attributed to the high level of engagement with 98 students at XSEDE16. Separately, and not included in this count, XSEDE staff talked with 50 students at the Tapia Conference who were interested in learning more about Blue Waters, XSEDE, and opportunities for students to become involved. Forty-two percent of new students benefiting from XSEDE resources and services are under- represented students. While this number falls short of meeting the goal of 50%, it mirrors the 42% of under-represented minorities (URMs) enrolled in our nation’s postsecondary education system of two-year through PhD professional degree granting institutions. Our 42% URM student users represents aggressive URM recruitment efforts considering only 19% of students enrolled in engineering are female, 8% are Hispanic or Latino, and 5% are African American or Black. The numbers are significantly lower in computer science. It is important to note that URM students benefiting from XSEDE resources and services represent 77% of new URM users. Overall, workforce development is on track to accomplishing its goals while working diligently to meet the targets. 4.3. User Engagement (WBS 2.1.3) The mission of the User Engagement (UE) team is to capture community needs, requirements and recommendations for improvements to XSEDE’s resources and services, and report to the national community how their feedback is being addressed. In PY6 and beyond, XSEDE will place greater emphasis on maintaining consistent user contact, traceability in tracking user issues, and closing the feedback loop. The UE team will track three area metrics: the number of active and new PIs that are contacted quarterly, the number of user issues that are identified via PI responses to the contact emails that require follow-up, and the number of user issues that are resolved within each reporting period. The UE team will maintain quarterly contact with all active research teams via email to the project PI to ensure the team is making progress, any obstacles to the team are being addressed,
PY6 IPR 1 Page 21 and user requirements are being gathered. Startup PIs will be contacted 15-45 days after their allocation start date and new and renewal project PIs will be contacted 15-30 days following the start of the new allocation period. Also, active PIs within their second, third and fourth quarters of their allocation period will be contacted in the first month of each allocation quarter. Issues that are raised in response to these contact emails will be logged and tickets generated on the user’s behalf if further action is required. Tickets will be handled by the UE team, assigned to another XSEDE team, or assigned to the appropriate Service Provider(s) for resolution. The UE team will track tickets to ensure resolution. UE will also use the ticket data to identify community needs and file Use Cases for further follow-up by the XCI area. Action items resulting from issues and community needs will be recorded and tracked along with the status and this information will be made available to the user community. The number of user issues/requirements resolved within each reporting period will be included in each quarterly report.
Table 4-3: Area Metrics for User Engagement
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Percentage of PY10 Sustain — active and new PY9 Provide PIs contacted excellent user PY8 support (§3.3.2) PY7 100% 100% PY6 /qtr (1533) Percentage of PY10 Sustain — user PY9 Provide requirements excellent user entered/tracked PY8 support (§3.3.2) PY7 100% 100% PY6 /qtr (32) Percentage of PY10 Sustain — user PY9 Provide requirements excellent user resolved PY8 support (§3.3.2) PY7 100% 50% PY6 /yr1 (16/32) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 Resolution may be dependent upon SPs and other XSEDE groups. All user contact emails scheduled within the reporting period were sent: August and September startup PIs, PIs with allocations that began on October 1, and all other PIs with active allocations (PIs in Q2, Q3, or Q4 of their allocation period). The percentage of user requirements resolved is well below our annual target of 100% but as noted, resolution of these items is dependent upon SPs and other XSEDE groups. A lesson learned is that contact emails will be sent earlier to PIs in the “all other” category to allow for resolving issues in a more timely fashion, resulting in more accurate metric reporting. 4.4. Broadening Participation (WBS 2.1.4) Broadening Participation will engage under-represented minority researchers from domains that are not traditional users of HPC and from Minority Serving Institutions. This target audience
PY6 IPR 1 Page 22 ranges from potential users with no computational experience to computationally savvy researchers, educators, Champions, and administrators that will promote change at their institutions for increased use of advanced digital services for research and teaching. Broadening Participation will continue the most effective recruitment activities— conference exhibiting, campus visits, and regional workshops—while increasing national impact through new partnerships and the utilization of lower cost awareness strategies to continue the growth in new users from under-represented communities. The Diversity Forum and the Minority Research Community listservs and community calls focus on user persistence in their use of XSEDE services and their deepening engagement through participation in committees such as the User Advisory Committee (UAC) and XRAC, participation in Champions, bridging, and other programs. Persistent institutional engagement is enabled by curriculum reform and larger numbers of researchers adopting the use of advanced digital resources as a standard research method. The “Number of new under-represented individuals using XSEDE resources and services” is defined as the number of first-time awardees of XSEDE compute resources or an “allocation” (i.e. Startup, Research, Education, etc.) from underrepresented communities including women and racial/ethnic domestic minorities in HPC as well as anyone from a Minority Serving Institution (MSI) as defined by the Carnegie Classification of Institutions of Higher Education are tracked. The “Number of sustained under-represented individuals using XSEDE resources and services” is the number of awardees of XSEDE compute resources or an “allocation” (i.e. Startup, Research, Education, etc.) from underrepresented communities including women and racial/ethnic domestic minorities in HPC as well as anyone from a Minority Serving Institution (MSI) as defined by the Carnegie Classification of Institutions of Higher Education are tracked that access their “allocation” during the time period. For both metrics, “Longitudinal assessment of inclusion in XSEDE” and “Longitudinal assessment of diversity in XSEDE”, a mean index score is generated from annual Staff Climate Study responses to items within the "Inclusion" and “Diversity” dimension of the study, respectively.
Table 4-4: Area Metrics for Broadening Participation
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of new PY10 Deepen/Exten under- PY9 d — Extend represented PY8 use to new individuals PY7 communities using XSEDE (§3.1.2) resources and PY6 >100/yr 150 services Number of PY10 Deepen/Exten sustained PY9 d — Extend under- PY8 use to new represented PY7 communities individuals (§3.1.2) using XSEDE >1,000 PY6 322 resources and /yr services Longitudinal PY10 Advance — Assessment of PY9 Enhance the Inclusion in PY8 array of XSEDE via the PY7 technical
PY6 IPR 1 Page 23 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Staff Climate expertise and 5% Study support PY6 improve- - services ment/yr (§3.2.2) Longitudinal PY10 Advance — Assessment of PY9 Enhance the Equity in PY8 array of XSEDE via the PY7 technical Staff Climate expertise and Study 5% support PY6 improve- - services ment/yr (§3.2.2) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. -Data reported annually. The number of new and sustained URM users are about 8% of all the new and sustained users, and the number of new URM users in the first two months exceeded the target for the year. Contributing factors include the strong recruitment and awareness at XSEDE16 and exhibiting at the ACM Richard Tapia Celebration of Diversity in Computing, a student focused conference. Thus, students constituted 77% of the new URM users. (See the student program metrics in the Workforce Development discussion in Section 4.2.) The number of sustained users is on track to meet or exceed the target. It is anticipated students will continue to be a larger percentage of the URM sustained users as URM student population is growing faster than the URM faculty population. The diversity forum is being established and the climate survey is conducted annually. Thus, there are no metrics to report at this time for inclusion and equity. 4.5. User Interfaces & Online Information (WBS 2.1.5) User Interfaces & Online Information (UII) is committed to enabling the discovery, understanding, and effective utilization of XSEDE’s powerful capabilities and services. UII has immediate impact on XSEDE users from day one, providing them with an information rich website, the XSEDE User Portal, and a uniform set of user documentation. The UII team will track six area metrics: number of new users of XSEDE resources and services, number of sustained users of XSEDE resources and services, number of unique visitors to the website and User Portal, and user satisfaction rating of the website, User Portal, and online documentation. The number of new users of XSEDE resources and services is comprised of the number of new XSEDE User Portal accounts that are created in PY6. The number of sustained users of XSEDE resources and services is the total number of users who have logged into the XSEDE User Portal during PY6, including new users that created accounts during PY6. The number of pageviews to the website and User Portal will be tracked using Google Analytics. This is defined as both new and returning users that have at least one session within the selected time period. User satisfaction of the website, User Portal and user documentation will be a rating from the annual XSEDE User Survey. If additional surveys such as micro-surveys are conducted to measure satisfaction, they will be shared in the appropriate quarterly report.
PY6 IPR 1 Page 24 Table 4-5: Area Metrics for User Interfaces & Online Information Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of new PY10 Deepen/Extend users of XSEDE — Extend use to PY9 resources and new services PY8 communities PY7 (§3.1.2) >1,000 PY6 1,881 /qtr Number of PY10 Deepen/Extend sustained users of — Deepen use to PY9 XSEDE resources existing and services PY8 communities PY7 (§3.1.1) >5,000 PY6 5,610 /qtr Number of PY10 Deepen/Extend pageviews to the — Raise PY9 XSEDE website Awareness of the PY8 value of PY7 advanced digital services (§3.1.4) 80,000 PY6 49,409 /qtr Number of PY10 Deepen/Extend pageviews to the — Raise PY9 XSEDE User Portal Awareness of the PY8 value of PY7 advanced digital services (§3.1.4) 100,000 PY6 183,408 /qtr User satisfaction PY10 Sustain — with website Provide excellent PY9 user support PY8 (§3.3.2) PY7 PY6 4 of 5/yr - User satisfaction PY10 Sustain — with user portal Provide excellent PY9 user support PY8 (§3.3.2) PY7 PY6 4 of 5/yr - User satisfaction PY10 Sustain — with user Provide excellent PY9 documentation user support PY8 (§3.3.2) PY7 PY6 4 of 5/yr - NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. - Data reported annually. The UII team has exceeded the metrics for the XSEDE User Portal and came close on the website. We believe the website did not meet its metrics due to the shortened quarter. We expect to meet
PY6 IPR 1 Page 25 the metrics on both of these for future quarters. For the remaining metrics that weren’t met we suspect this is also due to the shortened quarter and will evaluate any changes that need to be done if future quarters do not meet the mark. 4.6. Campus Engagement (WBS 2.1.6) The Campus Engagement program promotes and facilitates the effective participation of a diverse national community of campuses in the application of advanced digital resources and services to accelerate discovery, enhance education, and foster scholarly achievement. Campus Engagement, via the Campus Champions, works directly with institutions across the US both to facilitate computing and data-intensive research and education, nationally and with collaborators worldwide, and to expand the scale, scope, ambition and impact of these endeavors. This is done by increasing scalable, sustainable institutional uptake of advanced digital services from providers at all levels (workgroup, institutional, regional, national, international), fostering a broader, deeper, more agile, more sustainable and more diverse nationwide cyberinfrastructure ecosystem across all levels, and cultivating inter-institutional interchange of resources, expertise and support. Campus Engagement’s core approach is to assist researchers and educators in identifying and facilitating the use of the most appropriate advanced digital capabilities for their extant and emerging needs, including workgroup, institutional, regional, national and/or international level (which is anticipated in a subset of cases to include XSEDE resources). Campus Engagement’s activities include, but are not limited to, fostering the expansion of both the scale and the scope of the national and worldwide community of cyberinfrastructure practitioners, both via internal initiatives and in collaboration with other related efforts. CE also aims to assist with the establishment and expansion of consortia (for example, intra-state, regional, domain-specific) that collaborate to better serve the needs of their advanced computing stakeholders. XSEDE teams (e.g., Service Providers, diversity leadership, Champions) will be leveraged to both reach appropriate stakeholders and provide them with the information and capabilities they need. CE will also inform institutional leadership about the value proposition of advanced digital services and the impact of these capabilities on research and education outcomes, as a driver for stimulating investment across all scales. Metrics include the number of institutions with a Champion, the number of unique contributors to the Champion email list and the number of activities that (i) expand the emerging CI workforce and/or (ii) improve the extant CI workforce, participated in by members of the Campus Engagement team. The number of institutions with Champions represents the breadth of the reach of the Campus Engagement/Champion program. The email list metric demonstrates active involvement across institutions and shows the extent to which peer mentoring is valued. The number of activities that expand and/or improve the extant CI workforce demonstrates the efforts of the team to nurture continued development of the CI workforce across the community.
Table 4-6: Area Metrics for Campus Engagement
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Deepen/Extend Institutions with PY9 — Deepen use a Champion to existing PY8 communities PY7 (§3.1.1) PY6 225/yr 224
PY6 IPR 1 Page 26 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Deepen/Extend unique PY9 — Deepen use contributors to to existing the Champion PY8 communities email list PY7 (§3.1.1) (campuschampi PY6 50/yr 90 [email protected]) Number of PY10 Deepen/Extend activities that (i) PY9 — Deepen use expand the to existing emerging CI PY8 communities workforce PY7 (§3.1.1) and/or (ii) improve the extant CI workforce, participated in PY6 20/yr 10 by members of the Campus Engagement team NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Campus Engagement is on target to meet or exceed all metrics. Since the latter two are new metrics, we plan to adjust our targets after PY6 to better reflect our progress and goals.
PY6 IPR 1 Page 27 5. Extended Collaborative Support Services (WBS 2.2) The Extended Collaborative Support Service (ECSS) improves the productivity of the XSEDE user community both through successful, meaningful collaborations and well-planned training activities. The goal is to optimize applications, improve work and data flows, increase effective use of the XSEDE digital infrastructure and broadly expand the XSEDE user base by engaging members of under-represented communities and domain areas. The ECSS program provides professionals who can be part of a collaborative team—dedicated staff who develop deep, collaborative relationships with XSEDE users—helping them make best use of XSEDE resources to advance their work. These professionals possess combined expertise in many fields of computational science and engineering. They have a deep knowledge of underlying computer systems and of the design and implementation principles for optimally mapping scientific problems, codes, and middleware to these resources. ECSS includes experts in not just the traditional use of advanced computing systems but also in data-intensive work, workflow engineering, and the enhancement of scientific gateways. ECSS projects fall into five categories: Extended Support for Research Teams (ESRT), Novel and Innovative Projects (NIP), Extended Support for Community Codes (ESCC), Extended Support for Science Gateways (ESSGW), and Extended Support for Training, Education and Outreach (ESTEO). Project-based ECSS support is requested by researchers via the XSEDE peer-review allocation process. If reviewers recommend support and if staff resources are available, the ECSS expert and the requesting PI develop a work plan outlining the project tasks. The work plan includes concrete quarterly goals and staffing commitments from both the PI team and ECSS. ECSS managers review work plans and also track progress via quarterly reports. The main metrics of ECSS are the number of projects with workplans per year (targets are ESRT (30), ESCC (10), ESSGW (10)), the average satisfaction with the ECSS support as reported by PIs in post-project interviews (target is 4.5/5), and the impact that those PIs think the ECSS support has had on their project (target is 4/5). The other two metrics, number of new users from non- traditional disciplines of XSEDE resources and services and number of sustained users from non-traditional disciplines of XSEDE resources and services are explained in more detail just before Table 5-3.
Table 5-1: Area Metrics for Extended Collaborative Support Services Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of completed PY10 Deepen/Extend — ECSS projects PY9 Deepen use to (ESRT + ESCC + existing ESSGW) PY8 communities PY7 (§3.1.1) PY6 50/yr 10 Average ECSS impact PY10 Deepen/Extend — rating PY9 Deepen use to existing PY8 communities PY7 (§3.1.1) 4 of 5 PY6 4.56 /qtr Average satisfaction PY10 Deepen/Extend — with ECSS support PY9 Deepen use to existing PY8 communities PY7 (§3.1.1)
PY6 IPR 1 Page 28 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported 4.5 of 5 PY6 4.86 /qtr Number of new users PY10 Deepen/Extend — from non-traditional PY9 Extend use to new disciplines of XSEDE communities resources and PY8 (§3.1.2) services PY7 PY6 100/qtr 147 Number of sustained PY10 Deepen/Extend — users from non- PY9 Deepen use to traditional existing disciplines of XSEDE PY8 communities resources and PY7 (§3.1.1) services PY6 100/qtr 451
NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Individual area metrics are discussed below; however, across ECSS as a whole, we are on track to meet our targets. PI ratings for both satisfaction and impact have exceeded the targets in this reporting period. 10 completed projects over the 2 month reporting period puts us on track to exceed 50 completed projects per year. Highlights from individual projects and areas are described below. Digital Instrument for Sound Synthesis and Composition. DISSCO, a Digital Instrument for Sound Synthesis and Composition, offers a unified approach to music composition and sound synthesis, bringing both disciplines together in a seamless process. Presently, DISSCO consists of three modules: LASS, a Library for Additive Sound Synthesis; CMOD, a Composition Module; and LASSIE, a Graphical User Interface (GUI). Sever Tipei, Professor of Music at the University of Illinois at Urbana-Champaign and Manager of the Computer Music Project of the UIUC Experimental Music Studios, worked with ECSS NIP expert Alan Craig (Shodor) to develop a XSEDE startup project with the ultimate goal to run DISSCO in real time or faster (the amount of time for computation not to exceed the time of the performance) for high complexity of the underlying data/composition. The grant was awarded an allocation on SDSC Comet, and an ESRT project was launched with experts Paul Rodriguez and Bob Sinkovits (both SDSC) to analyze and optimize the CMOD module. Work to date has focused on compiler optimization and benchmarking multi-threaded execution, honing in on strategies to improve threaded scalability, as well as performance for a given number of threads. Some initial code changes already indicate the possibility of 2x speedup before the scaling issues are even addressed. Distributed Multi-Threaded CheckPointing. During the last reporting period, an active ESCC project hit a major milestone. In what appears to be the largest example of transparent checkpointing ever, the DMTCP software (Distributed Multi-Threaded CheckPointing) was used to checkpoint an application across 2,048 nodes on TACC's Stampede system. This effort was led by Ph.D. student, Jiajun Cao, in the College of Computer and Information Science at Northeastern University in collaboration with ECSS expert, Jerome Vienne (TACC). The exercise was performed using DMTCP to checkpoint the HPCG (High Performance Conjugate Gradients) program running on 32,768 CPU cores. It was using a total of 38 terabytes of RAM spread across 2,048 nodes. The application was checkpointed in 10 minutes and 53 seconds. A second program, NAMD (Scalable Molecular Dynamics), running on 16,368 cores (1,024 nodes), using a total of 10 terabytes, was checkpointed in two minutes and 38 seconds.
PY6 IPR 1 Page 29 Checkpoint-restart is the ability to save the state of a running program into a file, possibly copy that file to a new computer, and then restart it from the point at which it was checkpointed. This is a widely-used technology for scientists and engineers with long-running programs. The application developer can use DMTCP without any modification to their own application software. The effort represents a collaboration of researchers at Northeastern University, TACC/U. of Texas, The Ohio State University, and the University at Buffalo. Other notable highlights for this quarter include significant involvement from ESTEO in the design and development of new project management tools, Confluence and JIRA. These will be used throughout XSEDE, but perhaps most intensively in ECSS. A special focus includes the implementation of an ECSS workflow so that we can easily see the status of all projects, including those that need assignments, those that are missing workplans and those that are missing reports. Workplans are being implemented in Confluence with ties to JIRA so that staff can more easily track and update tasks. Leadership responsibilities for the International High Performance Computing Summer School have also shifted to ESTEO. 5.1. ECSS Director’s Office (WBS 2.2.1) The ECSS Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the ECSS area. The office consists of two Level 2 Co- Directors (Ralph Roskies, who manages ESRT and NIP activities, and Nancy Wilkins-Diehr who manages ESCC, ESSGW, and ESTEO activities) and two project managers (Karla Gendler and Marques Bland). The Level 2 ECSS Co-Directors, Roskies and Wilkins-Diehr, will continue to advise the XSEDE PI on many issues, especially those where ECSS activities are relevant. They monitor compliance with budgets, and retarget effort if necessary. They periodically run staff face-to-face meetings and respond to concerns, particularly those that emerge from staff climate surveys. In addition, Roskies and Wilkins-Diehr carry out the post-project interviews with all project PIs who have received ECSS support, both to get their assessment of how the project went, and to hear and act on any concerns they may express. Wilkins-Diehr also organizes the monthly symposium series, one of the contributors to staff training, and runs the Campus Champions Fellows program (§4.6). The two project managers aid in the management of the day-to-day activities of ECSS which includes the management of project requests (XRAC and startups), active projects, project assignments and staffing. They will refine the ECSS project lifecycle, further defining processes to aid in the management of over 100 active projects. They will also administer JIRA for the management and tracking of projects, both for the managers and directors of ECSS and for ECSS staff. They will provide ECSS information to the XSEDE Project Management (PM) office and relay information from the PM team to ECSS. They manage and attend bi-weekly ECSS management and less frequent full staff meetings as well as PM meetings. They post notes and action items to the ECSS wiki once meetings have concluded. They also maintain the ECSS wiki and mailing lists. New activities for PY6 include managing the transition to JIRA project management software. 5.2. Extended Support for Research Teams (WBS 2.2.2) Extended Support for Research Teams (ESRT) accelerates scientific discovery by collaborating with researchers, engineers, and scholars in order to optimize their application codes, improve their work and data flows, and increase the effectiveness of their use of XSEDE digital infrastructure.
PY6 IPR 1 Page 30 ESRT projects are initiated as a result of support requests or recommendations obtained during the allocation process. Most projects focus on home-grown codes, as community codes fall under ESCC (§5.4) but are not exclusively restricted to this classification. The primary mandate of ESRT is the support of individual research teams within the context of their research goals. The area metrics for ESRT include the 1) number of completed projects, 2) average impact rating, and 3) average satisfaction with support. The number of completed projects will be monitored throughout the project. A completed project is defined as a project that has progressed through the complete support pipeline, which includes steps such as assignment of a consultant, production of a work plan, execution of work plan and reporting of progress through quarterly reports, and the filing of a final project report. The PIs of these completed projects are contacted by ECSS leadership for an interview where PIs relay their experience with ECSS support and its impact upon their research. These discussions culminate in a numerical ranking (out of 5 possible) that expresses the level of satisfaction with ECSS support and its impact upon their research goals.
Table 5-2: Area Metrics for Extended Support for Research Teams
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Deepen/Extend completed PY9 — Deepen use ESRT projects to existing PY8 communities PY7 (§3.1.1) PY6 30/yr 3 Average ECSS PY10 Deepen/Extend impact rating PY9 — Deepen use to existing PY8 communities PY7 (§3.1.1) 4 of 5 PY6 5 /qtr Average PY10 Deepen/Extend satisfaction PY9 — Deepen use with ECSS to existing support PY8 communities PY7 (§3.1.1) 4.5 of 5 PY6 5 /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We are currently on track to meet our metrics. The average satisfaction and impact ratings exceed our goal. However, the number of completed projects is lower than the quarterly goal of ~7-8 projects. This is likely due to the shorter reporting period as well as a number of project extensions. There are currently 32 active projects in ESRT and this is expected to allow us to meet our metric as they are completed. 5.3. Novel and Innovative Projects (WBS 2.2.3) Novel and Innovative Projects (NIP) accelerates research, scholarship, and education provided by new communities that can strongly benefit from the use of XSEDE’s ecosystem of advanced digital services. Working closely with the XSEDE Outreach team, the NIP team identifies a subset of scientists, scholars and educators from new communities, i.e. from disciplines or demographics that have not yet made significant use of advanced computing infrastructure, who are now
PY6 IPR 1 Page 31 committed to projects that appear to require XSEDE services and are in a good position to use them efficiently. NIP staff then provides personal mentoring to these projects, helping them to obtain XSEDE allocations and use them successfully. XSEDE projects generated by, and mentored by, the personal efforts of the NIP experts should stimulate additional practitioners in their field to become interested in XSEDE. Strategies used will include building and promotion of science gateways serving communities of end-users and the enhancement of the Domain Champions program by which successful practitioners spread the word about the benefits of XSEDE to their colleagues. A set of 60 fields of science (FOS) have been identified in the XD Central Database, each of whose usage over the past 10 years is below 0.5% of the total normalized usage. The area metric “number of new users from non-traditional disciplines of XSEDE resources and services” (an XSEDE KPI) will report the total number of users on the projects newly activated from these FOS. The area metric “number of sustained users from non-traditional disciplines of XSEDE resources and services” (an XSEDE KPI) will report the total number of users on the projects from these FOS that have used at least 10% of their allocated usage. The area metric “number of new XSEDE projects from target communities generated by NIP” will report the number of projects from the targeted FOS that will be generated by the personal efforts of NIP staff members. The area metric “number of successful XSEDE projects from target communities mentored by NIP” will report the number of projects from the targeted FOS, personally mentored by NIP staff members that have used at least 10% of their allocated usage.
Table 5-3: Area Metrics for Novel and Innovative Projects Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of new PY10 Deepen/Extend users from non- PY9 — Extend use to traditional new disciplines of XSEDE PY8 communities resources and PY7 (§3.1.2) services PY6 100/qtr 147
Number of PY10 Deepen/Extend sustained users PY9 — Deepen use to from non- existing traditional PY8 communities disciplines of XSEDE PY7 (§3.1.1) resources and services PY6 100/qtr 451
Number of new PY10 Deepen/Extend XSEDE projects from PY9 — Extend use to target communities new generated by NIP PY8 communities PY7 (§3.1.2) PY6 20/yr 16 Number of PY10 Deepen/Extend successful XSEDE PY9 — Extend use to projects from target new communities PY8 communities mentored by NIP PY7 (§3.1.2) PY6 10/yr 23 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 32 NIP is on track to significantly exceed the targets we tentatively set when we redefined its metrics for XSEDE2. The generation and success (in terms of usage) of projects assisted by NIP staff is boosted by the availability of production SPs specifically designed to enable non- traditional applications (Bridges, Comet, Jetstream, Wrangler, Xstream). If these trends hold up over the course of PY6, we will increase the targets for PY7 accordingly. 5.4. Extended Support for Community Codes (WBS 2.2.4) Extended Collaborative Support for Community Codes (ESCC) extends the use of XSEDE resources by collaborating with researchers and community code developers to deploy, harden, and optimize software systems necessary for research communities to create new knowledge. ESCC supports users via requested projects and initiated projects. ESCC projects may be created in two different ways. Most ESCC projects are initiated as a result of requests for assistance during the allocation process. These projects are similar in nature to ESRT projects but involve community codes rather than codes developed for and by individual research groups. ESCC projects may also be initiated by staff to support a community’s needs. ESCC has three key metrics: number of completed projects, impact rating, and satisfaction rating. These metrics are collected on a quarterly basis although the targets are annual. The number of completed projects refers to the number of projects that were assigned, progressed to the workplan phase, and completed with a final report. The impact rating is an estimate of the impact the ESCC project had on a PI’s research based on a number the PI assigns to it. The satisfaction rating measures the PI’s satisfaction with the support provided by ECSS.
Table 5-4: Area Metrics for Extended Support for Community Codes
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Deepen/Extend completed PY9 — Deepen use ESCC projects to existing PY8 communities PY7 (§3.1.1) PY6 10/yr 3 Average ECSS PY10 Deepen/Extend impact rating PY9 — Deepen use to existing PY8 communities PY7 (§3.1.1) 4 of 5 PY6 4.67 /qtr Average PY10 Deepen/Extend satisfaction PY9 — Deepen use with ECSS to existing support PY8 communities PY7 (§3.1.1) 4.5 of 5 PY6 4 /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. ESCC is on track to meet the target of 10 completed projects per year with an impact rating that meets the target. However, the average satisfaction number is slightly below target. With the completion of only three projects for this period, it is too early to determine if this will be an issue for the coming project year.
PY6 IPR 1 Page 33 5.5. Extended Support for Science Gateways (WBS 2.2.5) Extended Support for Science Gateways (ESSGW) broadens science impact and accelerates scientific discovery by collaborating in the development and enhancement of science-centric gateway interfaces and by fostering a science gateway community ecosystem. ESSGW projects primarily begin through user requests from the XSEDE allocation process. Similar to ESRT and ESCC, ESSGW projects progress through three activities. First, the project is assigned to an ECSS expert. Second, the project is quantified with the formation of a work plan through collaboration with the research group. Third, when the project is completed, the ESSGW expert produces a final report with input from the research group. A successful project is the completion of all three phases. Each state of the progression is measured to provide an assessment of progress. Submission of work plans within 45 days of initial contact, 90% of projects with work plans completed, and 85% of completed projects with final reports within three months are additional criteria for success. Area metrics are direct measurements of ESSGW’s operations and effectiveness. The target of ten completed ESSGW projects per year measures ESSGW’s operations and indicates that the area is both bringing in new projects, and concluding previous consultations. The effectiveness of the consultations is measured by the next two metrics, impact rating and satisfaction of the support recipients. These two metrics are measured through interviews. The fourth metric measures the unique number of gateway users across all gateways.
Table 5-5: Area Metrics for Extended Support for Science Gateways Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Deepen/Extend — completed PY9 Deepen use to ESSGW projects existing PY8 communities PY7 (§3.1.1) PY6 10/yr 4 Average ESSGW PY10 Deepen/Extend — impact rating PY9 Deepen use to existing PY8 communities PY7 (§3.1.1) 4 of 5 PY6 4 /qtr Average PY10 Deepen/Extend — satisfaction with PY9 Deepen use to ESSGW support existing PY8 communities PY7 (§3.1.1) 4.5 of 5 PY6 5 /qtr Number of PY10 Deepen/Extend — unique gateway PY9 Deepen use to user existing PY8 communities PY7 (§3.1.1) 3,000 PY6 2694 /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 34 ESSGW is making solid progress on its area metrics and we expect to meet our yearly targets. Area focuses for next quarter will be to recruit new science gateway clients to replace expiring projects, to revise and update XSEDE’s website material on science gateways, and to work with ESTEO on new science gateway outreach and training materials. 5.6. Extended Support for Education, Outreach, & Training (WBS 2.2.6) Extended Collaborative Support for Training, Education & Outreach (ESTEO) prepares the current and next generation of researchers, engineers, and scholars in the use of advanced digital technologies by providing the technical support for Training, Education and Outreach planned activities. Typical events include train-the-trainers events, on-site classes requested by Campus Champions, regional workshops, conferences, and summer schools (including the International HPC Summer School). Staff also create and review online documentation and training modules. This on-demand training is increasingly popular with the user community when both time and travel budgets are limited. A few of the overall activities performed by ESTEO are represented in the Area Metrics table, namely, metrics surrounding support of the campus champions fellows program, a metric to gauge the number of live training events staffed, and metrics designed to measure breadth of attendees participating in both staff training and ECSS symposium activities. The full breadth of ESTEO activities is captured in the “ongoing activities” section below, which are measured and reported quarterly in the metrics appendix.
Table 5-6: Area Metrics for Extended Support for Education, Outreach, & Training Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of Campus PY10 Deepen/Extend — Champions fellows PY9 Prepare the current and next generation PY8 (§3.1.3) PY7 PY6 4/yr 5 Average Score of PY10 Deepen/Extend — fellows assessment PY9 Prepare the current and next generation PY8 (§3.1.3) PY7 4 of 5 PY6 4.33 /yr Number of live PY10 Deepen/Extend — training events PY9 Prepare the current staffed and next generation PY8 (§3.1.3) PY7 PY6 20/yr 7 Number of staff PY10 Deepen/Extend — training events PY9 Prepare the current and next generation PY8 (§3.1.3) PY7 PY6 2/yr 0 Attendees at staff PY10 Deepen/Extend – training events PY9 Prepare the current
PY6 IPR 1 Page 35 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported PY8 and next generation PY7 (§3.1.3) PY6 40/yr 0 Attendees at ECSS PY10 Deepen/Extend – Symposia PY9 Prepare the current and next generation PY8 (§3.1.3) PY7 PY6 300/yr 78 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We are on track to meet our annual metric goals, especially given the short reporting period. While there were no staff training events held this quarter, we are planning to hold such events in the upcoming quarters to meet our target of two events for the year.
PY6 IPR 1 Page 36 6. XSEDE Community Infrastructure (WBS 2.3) The mission of XSEDE Community Infrastructure (XCI) is to facilitate interaction, sharing, and compatibility of all relevant software and related services across the national CI community, building on, and improving upon, the foundational efforts of XSEDE. XCI envisions enabling users by targeting services allocated by XSEDE (including OSG resources), campus-based CI facilities, commercial cloud providers, CI software services such as science gateways and Globus Online, and even the individual researcher who wants to interact effectively with the national CI via her or his own laptop. Through XCI, XSEDE will serve an aligning function within the nation, not by rigorously defining a particular architecture, but rather by assembling a technical infrastructure that facilitates interaction and interoperability across the national CI community. The suite of interoperable and compatible software tools that XSEDE will make available to the community will be based on those already in use but will add additional services that address emerging needs including data and computational services. There are four area metrics for XCI: Average satisfaction rating of XCI services; number of new capabilities made available for production deployment; number of capabilities delivered vs. those planned; and total number of systems that use one or more CRI provided toolkits. For the average satisfaction rating of XCI services, the L2 Director and Deputy Director will focus their attention on current and new community stakeholders who can benefit from XCI services. This will be a critical method for demonstrating return on investment (ROI) for XCI. They will work with the XSEDE Evaluation team to survey all community stakeholders on the value of XCI services. For the number of new capabilities made available for production deployment, XCI will accept Use Cases from CEE, ECSS and Capability and Resource Integration (CRI) and will quickly provide capability development plans (CDP) that will be prioritized and executed based on feedback from the User Requirement Evaluation and Prioritization (UREP) and the SMT. Measuring the number of capabilities delivered versus those planned is essentially an efficiency ratio. With proper vetting and prioritization by the UREP and Senior Management Team (SMT), XCI is committed to the efficient and effective delivery of new capabilities. The total number of systems that use one or more CRI provided toolkits is a measure of effectiveness in broadening the community and participation in national cyberinfrastructure. If CRI is successful, participation in national cyberinfrastructure means sharing resources beyond institutional boundaries, not just consuming them.
Table 6-1: Area Metrics for XSEDE Community Infrastructure (XCI) Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Average PY10 Advance — Create satisfaction rating PY9 an open and of XCI services evolving e- PY8 infrastructure PY7 (§3.2.1) 4 of 5 PY6 4.8 /yr Number of new PY10 Advance — Create capabilities made PY9 an open and available for evolving e- production PY8 infrastructure deployment PY7 (§3.2.1) PY6 7/yr 0
PY6 IPR 1 Page 37 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Advance — Create capabilities PY9 an open and delivered/ Number evolving e- planned PY8 infrastructure PY7 (§3.2.1) PY6 1/yr 0 Total number of PY10 Advance — Create systems that use PY9 an open and one or more CRI evolving e- provided toolkits PY8 infrastructure PY7 (§3.2.1) PY6 450/yr 512 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We met or exceeded the target values for 2 of our 4 area metrics. We did not meet our target values for “Number of new capabilities made available for production deployment” or “Number of capabilities delivered/Number planned.” In both cases, this is because we have not fully delivered any XSEDE2 Use Cases because we have been finishing up XSEDE1 deliverables. XCI delivered the new Community Software Repository (XCSR) to enable the broader XSEDE community to share and discover software information, and to facilitate integration of new software into the XSEDE ecosystem. XCI will continue to expand on the initial XCSR features thru the remainder of PY6. We also delivered significant enhancements to three XSEDE software- based capabilities: interactive shell-based login using GSI OpenSSH client/server software, authenticating to XSEDE using Federated InCommon campus credentials using CI Logon, and improved job start prediction services using Karnak. CRI has made significant strides in integrating the Service Provider (SP) coordination services with former Campus Bridging activities. Victor Hazlewood continues to work closely with the SPs, especially with unallocated SPs. As of this IPR, significant numbers of unallocated SPs have provided information into the Resource Description Repository (RDR) and are preparing to submit additional information to the XSEDE information services framework. CRI continues its activities to facilitate the easy distribution of jobs between XSEDE and campus resources. The CRI field engineer worked closely with XSEDE Science Gateways to create a persistent cluster within the Jetstream resource for running jobs submitted via the SEAGrid gateway. In addition, cooperative work to create a campus queue that can submit jobs to OSG and to Torque/PBS-scheduled resources has been completed and the campus queue is in production, running as many as 10,090 concurrent jobs via the campus queue service. Outreach work continues to recruit new adopters of campus toolkits and cluster tools. The CRI team conducted a webinar in October in order to familiarize interested users with the toolkits available. Further calls were conducted with California State University Channel Islands, University of Missouri Kansas City, and Slippery Rock University to discuss deployments of CRI campus toolkits. 6.1. XCI Director’s Office (WBS 2.3.1) The XCI Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the XCI area. This oversight includes providing direction to the L3 management team, coordination of, and participation in, XCI planning activities and reports through the area’s Project Manager. The Director’s Office also attends and supports the preparation of project level reviews and activities.
PY6 IPR 1 Page 38 XCI will work with CEE, ECSS, and UREP groups to understand new Use Case requests and their priorities to ensure timely delivery of necessary capabilities and services. Furthermore, XCI will work with these same groups to help quantify the importance and usage of new services and capabilities by the NSF community in determining return on investment. The XCI Director’s Office will continue to manage and set direction for XCI activities and responsibilities. They will contribute to, and attend, bi-weekly SMT calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF, and attend XSEDE quarterly and annual meetings. The XCI Director’s office will also initiate, evaluate, and act on annual stakeholder satisfaction surveys. New activities include a renewed focus on identifying new community stakeholders who can benefit from XCI services, understanding their requirements, and measuring their satisfaction qualitatively and, wherever possible, quantitatively. 6.2. Requirements Analysis & Capability Delivery (WBS 2.3.2) The Requirements Analysis & Capability Delivery (RACD) team facilitates the integration, maintenance, and support of cyberinfrastructure capabilities addressing user technical requirements. The process begins by preparing Capability Delivery Plans (CDPs) that describe the technical gaps in XSEDE’s prioritized Use Cases. To fill the gaps, RACD evaluates and/or tests existing software solutions, engages with software providers and facilitates software and service integration. To ensure software and service adoption and ROI, RACD will involve users, Service Providers and operators in an integration process that uses engineering best practices and instrument components to measure usage. Once components are integrated, RACD will facilitate software maintenance and enhancements in response to evolving user needs and an evolving infrastructure environment. RACD helps create an open and evolving e-infrastructure by integrating components that support XSEDE Use Cases. The first key activity in that process involves preparing Capability Delivery Plans (CDPs) that detail how RACD plans to address software capability gaps in XSEDE prioritized Use Cases. Some capability gaps may require engaging with current or new software partners and this number of engagements is to be tracked. Based on lessons learned in the previous years of the project, the engineering process further improves both user and service provider engagement during the capability integration process and thus the satisfaction of this engagement is tracked.
Table 6-2: Area Metrics for Requirements Analysis & Capability Delivery Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of PY10 Advance — Create capability delivery PY9 an open and plans prepared for evolving e- prioritized Use PY8 infrastructure Cases PY7 (§3.2.1) PY6 7/yr 9 Number of CI PY10 Advance — Create integration PY9 an open and assistance evolving e- engagements PY8 infrastructure PY7 (§3.2.1) PY6 6/yr 4 User rating of PY10 Advance — Create components PY9 an open and
PY6 IPR 1 Page 39 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported delivered in PY8 evolving e- production PY7 infrastructure (§3.2.1) 4 of 5 PY6 51 /yr Operator rating of PY10 Sustain — Provide components PY9 reliable, efficient, and secure delivered for PY8 production infrastructure deployment PY7 (§3.3.1) 4 of 5 PY6 52 /yr Software/service PY10 Advance — Create provider rating of PY9 an open and our integration evolving e- assistance PY8 infrastructure PY7 (§3.2.1) 4 of 5 PY6 53 /yr Responsiveness to PY10 Advance — Create defect and support PY9 an open and requests evolving e- PY8 infrastructure PY7 (§3.2.1) 45 days 7 PY6 or days less/yr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 RACD delivered 5 components this period, one targeting internal developers and four user facing. Of the user facing, only one was available to users long enough to collect user ratings. We will report on the others in future progress reports. 2 RACD delivered 5 components this period, three operated by us/RACD and two by XSEDE and SP Operations. Of those two delivered to XSEDE and SP Operations, only one was available long enough to collect an operator rating. 3 RACD has four active assistance engagements, one that is new and too early to rate and three that are ongoing. One engagement gave us a rating while the other two did not have notable interactions with us during this reporting period. We met or exceeded the target for all of our metrics. 6.3. XSEDE Capability & Resource Integration (WBS 2.3.3) The mission of the Capability & Resource Integration (CRI) team is to work with SPs, CI providers and campuses to maximize the aggregate utility of national cyberinfrastructure. CRI will coordinate interactions between SP’s and XSEDE in the SP Forum, engage with national CI providers, gather requirements for tools and training that assist users of XSEDE and other CI, identify and disseminate benefits and costs of interoperating with XSEDE, and create toolkits that fit and improve common usage modalities. CRI’s activities are reflected in the uptake of CRI-integrated toolkits, such as the XSEDE Campus Bridging Cluster toolkit and XSEDE National Integration Toolkit, but also Globus Transfer clients and other toolkits as developed. The Area Metrics reflect users of CRI toolkits as well as integration of toolkits on Level 1 SP resources and the aggregate CI (in Teraflops) taking advantage of CRI tools.
PY6 IPR 1 Page 40 The number of systems using one or more CRI provided toolkits includes systems using the Globus Transfer software, the XSEDE National Integration Toolkit (XNIT), and XSEDE Campus Bridging Cluster (XCBC), as well as any new toolkits developed by CRI. The number of Level 1 total systems that incorporate tools reflects the level of adoption of CRI toolkits within the SP community. Thanks to incorporating SP Forum coordination, CRI hopes to get SP input on requirements for potential new toolkits, which paves the way towards a broader CI utility set. The number of repository subscribers indicates the number of campus sites which receive regular updates from the XSEDE Community Software Repository, indicating regular and prolonged use of the XNIT. Aggregate Teraflops of cluster systems reflects the uptake of both the XNIT and XCBC software and gives some measure of the overall capability provided by these toolkits. Metrics for partnership interactions reflect sustained work on the part of CRI with other CI providers, campuses, and SP representatives to be responsive to a broad set of needs.
Table 6-3: Area Metrics for XSEDE Capability & Resource Integration Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Total number of PY10 Advance — Create systems that use PY9 an open and one or more CRI evolving e- provided toolkits PY8 infrastructure PY7 (§3.2.1) PY6 450/yr 512 Number of Level 1 PY10 Advance — Create total systems that PY9 an open and fully incorporate evolving e- all of the PY8 infrastructure recommended PY7 (§3.2.1) tools from the XSEDE Community PY6 5/qtr 6/6 Repository Number of PY10 Advance — Create repository PY9 an open and subscribers to CRI evolving e- cluster and laptop PY8 infrastructure toolkits PY7 (§3.2.1) PY6 150/yr 91 Aggregate number PY10 Advance — Create of TeraFLOPS of PY9 an open and cluster systems evolving e- using CRI toolkits PY8 infrastructure PY7 (§3.2.1) 1,000/ length PY6 100 of project Number of PY10 Advance — Create partnership PY9 an open and interactions evolving e- between CRI and PY8 infrastructure SPs, national CI PY7 (§3.2.1) organizations, and campus CI PY6 12/yr 3 providers NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Targets were met for metrics in most areas, and we exceeded the target for the number of systems using CRI toolkits. The metric representing the aggregate number of TeraFLOPS
PY6 IPR 1 Page 41 counted is a project duration metric (we hope to reach 1000 by the end of the project); we will keep a running tally of this throughout the project. The metric representing the number of repository subscribers to CRI cluster and laptop toolkits is below target, but we are working to improve this by establishing some software toolkits that will allow for automated reporting of clusters, as well as having more direct contact with campus system maintainers through a new mailing list and area on the XSEDE User Portal. For partnership interactions, we expect to have 3 meetings with target audiences each quarter, bringing us to the target of 12 for the year with no problems.
PY6 IPR 1 Page 42 7. XSEDE Operations (WBS 2.4) The mission of XSEDE Operations is to install, connect, maintain, secure, and evolve an integrated cyberinfrastructure that incorporates a wide range of digital capabilities to support national scientific, engineering, and scholarly research efforts. In addition to the Operations Director’s Office (§7.1), Operations staff is subdivided into four teams based on the work breakdown structure: Cybersecurity (§7.2), Data Transfer Services (§7.3), XSEDE Operations Center (XOC) (§7.4), and Systems Operational Support (§7.5). The Operations management team meets weekly and individual Operations groups meet approximately bi-weekly with all meeting minutes posted to the XSEDE wiki. XSEDE Operations has three Area Metrics: (1) average composite availability (as a geometric mean) of both critical central services and the XSEDE Resource Allocation Service (XRAS) (2) hours of downtime of critical central services or multiple Service Providers resulting from a security incident with direct user impact, and (3) mean time to resolution (MTTR) of external user tickets. Maintaining and evolving an integrated cyberinfrastructure requires the availability, reliability, and security of digital resources. To that end, XSEDE Operations is charged with monitoring a number of hardware and software services. Along with monitoring and maintaining resources, the XOC provides XSEDE with effective and efficient user support by responding to service requests from end users and staff. All user (non- staff) service requests handled by the XOC and WBS ticket queues comprise this metric.
Table 7-1: Area Metrics for Operations
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Average PY10 Sustain — composite PY9 Provide availability of reliable, core services PY8 efficient, and (geometric PY7 secure mean of infrastructure critical (§3.3.1) PY6 99%/qtr 99.9 services and XRAS) Hours of PY10 Sustain — downtime PY9 Provide with direct reliable, user impacts PY8 efficient, and from an PY7 secure XSEDE infrastructure security PY6 <24/qtr 0 (§3.3.1) incident. Mean time to PY10 Sustain — ticket PY9 Provide resolution by excellent user XOC and WBS PY8 support ticket queues PY7 (§3.3.2) (hrs) PY6 <24/qtr 24.0 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 43 Average composite availability of core services was nearly 100%. There was no downtime of services resulting from security incidents. The MTTR for the XOC and WBS tickets was 24 hours, which is right at the target. In response to user feedback, we have begun collecting SSH key fingerprints for all relevant XSEDE allocated resources and publishing them on the main resource page of the XSEDE website. This helps users know that there is not a man-in-the-middle attack when either server keys change or when they log in for the first time. In response to staff feedback, we transitioned from a hardware token-only two factor authentication system, to a mix of hardware and software token two factor system. This increases resource administrator flexibility while maintaining a high level of security. We began conversations with Stanford network engineers to initiate the process of integrating Stanford’s campus into our XSEDEnet networking infrastructure. We also introduced a new area metric, which gauges user satisfaction with external user tickets closed by the XSEDE Operations Center (XOC). This metric directly reflects the quality of user experience with problem resolution and is a valuable feedback mechanism for Operations, especially the XOC staff. It is reported in the XOC section below. 7.1. Operations Director’s Office (WBS 2.4.1) The Operations Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the Operations area. This oversight includes providing direction to the L3 management team, coordination of, and participation in, Operations planning activities and reports through the area’s Project Manager. The Director’s Office also attends and supports the preparation of project level reviews and activities. The Operations Director’s Office will continue to manage and set direction for Operations activities and responsibilities. They will contribute to, and attend, bi-weekly senior management team calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF and attend XSEDE quarterly & annual meetings. The Director’s Office will also provide analysis of network traffic and user help ticket data to improve capabilities, service, and/or metrics. 7.2. Cybersecurity (WBS 2.4.2) The Cybersecurity Security (Ops-Sec) group protects the confidentiality, integrity and availability of XSEDE resources and services. The Area Metric for the Cybersecurity group is the percentage of time that critical XSEDE resources are unavailable due to a security incident. Downtime resulting from security incidents has a direct impact on the availability of critical XSEDE resources to users and is the key evaluative measurement of the Cybersecurity group’s efforts. Users expect XSEDE resources to be reliable and secure, thus the security team’s goal is to minimize any interruption of services related to a security event.
Table 7-2: Area Metrics for Cybersecurity
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Hours of PY10 Sustain — downtime with PY9 Provide direct user reliable, impacts from PY8 efficient, and an XSEDE PY7 secure
PY6 IPR 1 Page 44 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported security infrastructure PY6 <24/qtr 0 incident. (§3.3.1) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Because there were no security incidents, there was no downtime with user impact. 7.3. Data Transfer Services (WBS 2.4.3) The Data Transfer Services (DTS) group facilitates data movement and management for the community by maintaining and continuously evolving XSEDE data services and resources. The Area Metric for the DTS group is the performance (Gbps) of intra-XSEDE GridFTP transfers > 1GB. The metric for data transfer performance on large files reflects the ability to consistently provide high performance data movement facilities.
Table 7-3: Area Metrics for Data Transfer Services
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Performance PY10 Sustain — (Gbps) of PY9 Provide instrumented, reliable, intra-XSEDE PY8 efficient, and transfers > PY7 secure 1GB infrastructure PY6 1 Gbps/qtr 1.6 (§3.3.1) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We slightly surpassed the metric target this reporting period. 7.4. XSEDE Operations Center (WBS 2.4.4) The XSEDE Operations Center (XOC) staff serve as user advocates, providing timely and accurate assistance to the XSEDE community, while simultaneously monitoring and troubleshooting user- facing systems and services. The XOC has two Area Metrics. The mean time to ticket resolution (MTTR) for XSEDE user service requests in the XOC queue demonstrates how responsive and efficient the XOC is when handling and resolving tickets. This metric does not apply to WBS ticket queues, Service Provider ticket queues or to internal tickets issued to XSEDE staff or other XSEDE funded individuals. The user satisfaction metric directly gauges users opinions of their experience with the XOC.
Table 7-4: Area Metrics for XSEDE Operations Center
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Mean time to PY10 Sustain — resolution in PY9 Provide XOC queue excellent user PY8 support PY7 (§3.3.2) PY6 <24/qtr 4.2 User PY10 Sustain — satisfaction PY9 Provide
PY6 IPR 1 Page 45 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported with tickets PY8 excellent user closed by the PY7 support XOC (§3.3.2) PY6 4 of 5/qtr 4.8 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. Targets for both metrics were met and actually exceeded. We will continue tracking these and re-evaluate for PY7. 7.5. System Operations Support (WBS 2.4.5) Systems Operational Support (SysOps) provides enterprise level support and system administration for all XSEDE central services. The Area Metric for the SysOps group is the percentage of time that enterprise XSEDE services (a geometric mean) are available. The availability metric shows the amount of time that the critical enterprise services (Tier 1) are available for use. Services are grouped into criticality tiers based on their significance and dependence that other services have on them. Tier 1 systems are systems such as Kerberos, XDCDB (XSEDE Central Database), and DNS (Domain Name Service). Tier 2 are business-day supported services such as JIRA. Tier 3 are development supported systems such as the RT test instance. When calculated as a geometric mean, the availability metric will show the cumulative impact of the enterprise services to the stakeholders.
Table 7-5: Area Metrics for System Operations Support
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Average PY10 Sustain — availability of PY9 Provide critical reliable, enterprise PY8 efficient, and services (%) PY7 secure [geometric infrastructure PY6 99%/qtr 99.9 mean] (§3.3.1) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. We had nearly 100% availability of critical enterprise services this reporting period.
PY6 IPR 1 Page 46
8. Resource Allocation Service (WBS 2.5) The Resource Allocation Service (RAS) is building on XSEDE’s current allocation processes and evolving to meet the challenges presented by new types of resources to be allocated via XSEDE, new computing and data modalities to support increasingly diverse research needs, and large- scale demands from the user community for limited XSEDE-allocated resources. RAS is pursuing these objectives through three activities: managing the XSEDE allocations process in coordination with the XD Service Providers, enhancing and maintaining the RAS infrastructure and services, and anticipating changing community needs. The area metrics for RAS focus on support for two of XSEDE’s key sub-goals: providing excellent user support and providing reliable, efficient and secure infrastructure. An updated quarterly survey of users who have interacted during the quarter with the allocations request system and the allocations process more generally is being used to determine the user satisfaction ratings metrics. The survey has been developed with, and is coordinated through, the XSEDE Evaluation Team. The availability of XDCDB and XRAS will be measured by tracking the planned and unplanned outages for the XDCDB server on which XRAS and many other XSEDE services rely.
Table 8-1: Area Metrics for Resource Allocation Service
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Average user PY10 Sustain — satisfaction PY9 Provide rating with excellent user allocations and PY8 support other support PY7 (§3.3.2) services 4 of 5 PY6 3.98 /qtr Availability of PY10 Sustain — XRAS PY9 Provide reliable, PY8 efficient, and PY7 secure 99% infrastructure PY6 99.9% /qtr (§3.3.1) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. The "Average user satisfaction rating with allocations and other support services" is a composite metric comprised of user satisfaction ratings of the allocations process (Table 8-2) and of XRAS (Table 8-3). This metric was just below our quarterly target. User satisfaction survey results of the XRAS system were just shy of being within our target range. While the users are generally quite satisfied with the overall use of XRAS, the allocations satisfaction survey results indicate a need for improvements to the online documentation for the use of XRAS. This documentation (and allocations documentation more generally) will be a focus for RAS in the upcoming reporting period. User satisfaction survey results with the allocations process were also just short of the target. This value is calculated from the quarterly allocations satisfaction survey. Satisfaction with the process is the average across a 10-part question that covers aspects of the allocations process, from the policies in general, documentation, user effort, and outcomes. The first such survey indicated that time spent preparing supporting documents (3.82) and usefulness of reviewer comments (3.57) are the current areas with lowest user satisfaction; six of the 10 components had satisfaction ratings above 4.0. RAS will present the results to XRAC members and XSEDE’s ECSS reviewers to start bringing up the satisfaction with reviewer
PY6 IPR 1 Page 47 comments. Recommendations from the allocation policy review process are expected to help address the document preparation issue, and we will prioritize relevant recommendations for earliest implementation. The target for the availability of XRAS was exceeded. The entire RAS group participated in a two-day onsite planning meeting hosted at NCAR. This meeting culminated in a five-year plan for the area, an introduction to Agile methods, and a refinement of the process by which features and improvements are approved for implementation in the Allocations, Accounting & Account Management (A3M) infrastructure. The process includes the use of JIRA for issue management using the SCRUM framework. Two sprints have been conducted, leading to improved communication among the members of the group, with XSEDE management and stakeholders. The primary features added during this period include improved XDCDB security, XRAC meeting booklet generation, as well as research into a Continuous Integration environment. 8.1. RAS Director’s Office (WBS 2.5.1) The RAS Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the RAS area. This oversight includes providing direction to the L3 management team, coordination of, and participation in, RAS planning activities and reports through the area’s Project Manager. The Director’s Office also attends and supports the preparation of project level reviews and activities. The RAS Director’s Office will continue to manage and set direction for RAS activities and responsibilities. They will contribute to and attend bi-weekly senior management team calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF and attend XSEDE quarterly and annual meetings. 8.2. XSEDE Allocations Process & Policies (WBS 2.5.2) Allocations enable the national open science community to easily gain access to XSEDE’s advanced digital resources, allowing them to achieve their research and education goals. In support of the goal “Sustain – Provide excellent user support,” Allocations has two area metrics: user satisfaction with the allocations process and the average time to process Startup requests. Allocations also supports the goal “Provide reliable, efficient, and secure infrastructure” by measuring the percentage of XRAC-recommended service unit (SUs) allocated, which is a third Area Metric for this group. This third metric provides insight for RAS, the XSEDE program, NSF, and Service Providers into the success of the XSEDE ecosystem at meeting users’ scientific objectives.
Table 8-2: Area Metrics for XSEDE Allocations Process & Policies
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported User PY10 Sustain — satisfaction PY9 Provide with excellent user allocations PY8 support process PY7 (§3.3.2) 4 of 5 PY6 3.97 /qtr Average time PY10 Sustain — to process PY9 Provide
PY6 IPR 1 Page 48 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Startup PY8 excellent user requests PY7 support (§3.3.2) 2 weeks 10.65 PY6 or less days /qtr Percentage of PY10 Sustain — XRAC- PY9 Provide recommended excellent user SUs allocated PY8 support PY7 (§3.3.2) 100% PY6 62% /qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. User satisfaction with the allocations process was just below target. This value is calculated from the quarterly allocations satisfaction survey. Satisfaction with the process is the average across a 10-part questionnaire that covers various aspects of the allocations process, from the policies in general, documentation, user effort, and outcomes. The first such survey indicated that time spent preparing supporting documents (3.82) and usefulness of reviewer comments (3.57) are the current areas with lowest user satisfaction; six of the 10 components had satisfaction ratings above 4.0. RAS will present the results to XRAC members and XSEDE’s ECSS reviewers to start bringing up the satisfaction with reviewer comments. Recommendations from the allocation policy review process are expected to help address the document preparation issue, and we will prioritize relevant recommendations for earliest implementation. The target for average time to process startup requests was met. The percentage of XRAC-recommended SUs allocated was below target. Though data collected by RAS provides insight to XSEDE, NSF, and other stakeholders, this metric underscores the challenges faced by the ecosystem as a whole and is not wholly within the ability of RAS to control. However, it does reflect the challenge faced by RAS in maintaining satisfaction with the allocations process overall. RAS is continuing to look for additional metrics to summarize aspects of RAS’s conduct of the allocations process. 8.3. Allocations, Accounting, & Account Management CI (WBS 2.5.3) The Allocations, Accounting & Account Management CI (A3M) group maintains and improves the interfaces, databases and data transfer mechanisms for XSEDE-wide resource allocations, accounting of resource usage, and user account management. The Area Metrics for A3M are satisfaction results from surveys on the use of the XSEDE Resource Allocation Service (XRAS)— the primary user-facing service for RAS—the aggregated availability of XDCDB and XRAS systems, and the percentage of approved feature change requests implemented. The activities in A3M support the XSEDE sub-goals of providing a reliable and secure infrastructure, and creating an open and evolving e-infrastructure.
Table 8-3: Area Metrics for Allocations, Accounting, & Account Management CI
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported User PY10 Sustain — satisfaction PY9 Provide
PY6 IPR 1 Page 49 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported with XRAS PY8 excellent user system PY7 support (§3.3.2) 4 of 5 PY6 3.98 /qtr Availability of PY10 Sustain — the XRAS PY9 Provide systems reliable, PY8 efficient, and PY7 secure 99% infrastructure PY6 99.9% /qtr (§3.3.1) Percentage of PY10 Sustain — approved PY9 Provide feature change reliable, requests PY8 efficient, and implemented PY7 secure 100% 100% infrastructure PY6 /qtr (7/7) (§3.3.1) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. The survey results for user satisfaction with XRAS systems were just shy of being within our target range. While the users are generally quite satisfied with the overall use of XRAS, the XRAS satisfaction survey results indicate a need for improvements to the online documentation for the use of XRAS. This will be a focus for the A3M group in the upcoming reporting period. The targets for the availability of the XRAS systems and percentage of approved feature change requests implemented were met.
PY6 IPR 1 Page 50 9. Program Office (WBS 2.6) The purpose of the Program Office is to ensure the critical project level functions are in place and operating effectively and efficiently. The oversight, provided via the Project Office, is necessary to provide consistent guidance and leadership to the L3 managers across the project. A common and consistent approach to managing projects and risks is provided by the Project Management, Reporting and Risk Management (PM&R) team, while Business Operations manages all financial and sub-awards. The crucial aspect of communications to all stakeholders is the focus of the External Relations team. Finally, Strategic Planning, Policy and Evaluation (SP&E) focuses attention in precisely those areas to ensure the best possible structure continues to exist within XSEDE to allow the support of all significant project activities and enable efficient and effective performance of all project responsibilities. As with other areas of the project, the Program Office has established Area Metrics to track performance against attaining the project’s strategic goals. The Area Metrics for “Social Media,” and “Media Hits” have been separated as a way to show the impact of each primary activity with one number best representing the performance each quarter and year. “Social Media” refers to “impressions,” which is more comprehensive than just “followers” or “likes,” but includes the amount of people who may see content based on people sharing XSEDE’s content to their friends and followers on both Facebook and Twitter. “Media Hits” refers to the number of stories that were shared or written by external print or digital publications. To deliver the most advanced and useful digital services for researchers, XSEDE must constantly evolve. This makes continuous evaluation and improvement of processes and policies critical to the effectiveness of XSEDE as an organization; therefore, as tracked by the PM&R team, the project’s KPI for sustaining an effective virtual organization is the number of improvements made. Potential improvements consist of recommendations based on the staff climate survey, efforts by area managers to increase efficiency or quality, adaptations in response to an evolving project, or improvements to overall execution through implementation of widely validated methodologies. Measuring innovation for an organization like XSEDE (or for organizations in general) is difficult and represents an area of open research. A partial measure is the number of staff publications produced since this shows that XSEDE staff is involved in novel activities that achieve peer- reviewed publication. Additionally, after much thought and discussion both internally and with external stakeholders and advisors, XSEDE has identified two additional indicators that strongly correlate to innovation: 1) the ratio of proactive organizational improvements to those that were reactive and 2) the number of improvements that are innovative or lead to innovations. The first indicator is a measurement of organizational maturity and agility; the second measures innovative actions directly. This will continue to be an open conversation within the organization and with XSEDE’s stakeholders and advisors as XSEDE assess these measurements and how to best to qualify innovation.
Table 9-1: Area Metrics for Program Office Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported PY10 Deepen/Extend PY9 — Raise awareness of the PY8 value of Number of Social PY7 advanced digital Media impressions 228,000 services (§3.1.4) PY6 52,500 /yr
PY6 IPR 1 Page 51 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of media PY10 Deepen/Extend hits PY9 — Raise awareness of the PY8 value of PY7 advanced digital PY6 147/yr 32 services (§3.1.4) Percentage of PY10 Sustain — recommendations PY9 Operate an addressed by effective and relevant project PY8 productive areas PY7 virtual organization PY6 90%/yr NA1 (§3.3.3) Number of strategic PY10 Sustain — or innovative PY9 Operate an improvements innovative PY8 virtual PY7 organization PY6 9/yr 3 (§3.3.4) Ratio of proactive PY10 Sustain — to reactive PY9 Operate an improvements innovative PY8 virtual PY7 organization PY6 3/qtr 2:1 (§3.3.4) Number of staff PY10 Sustain — publications PY9 Operate an innovative PY8 virtual PY7 organization PY6 70/yr 5 (§3.3.4) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 L2 Directors are currently responding to climate study recommendations; data will be available in IPR2. The first two months of the new XSEDE award has been focused on the initial setup activities, and the creation of plans for internal operations and raising awareness of the XSEDE project. Initial setup activities are across the entire Program Office. Business Operations has made significant progress with the processing of the initial sub-awards for all partner institutions. All sub-awards are expected to be in place by the end of 2016. The creation and integration of improved financial management tools are progressing, with full integration of the tools expected by the end of 2016. Business operational lessons learned from PY5 have been evaluated and improvements implemented to increase the communication and transparency of the XSEDE Business Operations activities to the sub-award PIs and associated business offices. The PM&R team has implemented a new reporting process that is expected to increase content owner collaboration while decreasing the overall overhead of the reporting process. One major project that has been implemented and nearing completion is the transition to the new, publicly- viewable XSEDE wiki. The wiki has been established, largely populated, and is expected is to be complete in the initial population of the individual level 2 and level 3 wiki pages by the end of 2016. The SP&E team has been focused on the creation of the XSEDE Performance Management Plan. The committed delivery of the completed plan is February, 2017. External Relations has focused on the objective of increasing the awareness of the XSEDE project within the research and computational science communities. Notable activities towards this
PY6 IPR 1 Page 52 objective include preparation for the 2016 Supercomputing Conference (SC16) and the creation of multimedia content that includes information about XSEDE Level 1 Service Providers as well as interviews with researchers that have active XSEDE allocations. The finalized multimedia set will be used at SC16, as well as throughout XSEDE communications via the XSEDE website and newsletters. 9.1. Project Office (WBS 2.6.1) The Project Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the Program Office area and to establish responsibility for assuring advisory activities of the project occur. This oversight includes providing direction to the L3 management team and coordination of and participation in Program Office planning activities and reports through the area’s Project Manager. The Project Office also attends and supports the preparation of project level reviews and activities. Importantly, the Project Office is also responsible for assuring that the XSEDE Advisory Board, the User Advisory Committee, and the SP Forum are functioning. The Project Office is also responsible for coordination of project- level meetings such at the bi-weekly Senior Management Team (SMT) teleconference calls and the project quarterly meetings. With the transition to a new organizational structure for PY6, the makeup of the Senior Management Team (SMT) has changed. The XSEDE SMT is the highest-level management body in the organization. It is chaired by the Program Director (PI Towns), and includes the WBS Level 2 directors of Community Engagement & Enhancement (co-PI Gaither), the Extended Collaborative Support Service (Co-PI Wilkins-Diehr and Co-PI Roskies), XSEDE Community Infrastructure (Lifka), XSEDE Operations (Peterson), the Resource Allocations Service (Hart) and the Program Office (Payne). In order to be responsive to both the user community and the set of collaborating SPs, the chairs of the User Advisory Committee (currently Emre Brookes, University of Texas Health Science Center at San Antonio) and the XD Service Providers Forum (currently Dan Stanzione, University of Texas at Austin) are members of the SMT. These ten individuals constitute the voting members of the SMT. The Senior Project Manager (Gendler) is an ex officio member of the SMT. The cognizant NSF Program Officer (Eigenmann) is also an ex officio member. The XSEDE Senior Management team meets on a bi-weekly basis to assess project status and plans, and to address issues. 9.2. External Relations (WBS 2.6.2) External Relations’ (ER) mission is to communicate the value and importance of XSEDE to all stakeholders (including the internal audience) through creative and strategic communications. The ER team will measure its ability to effectively communicate with internal and external stakeholders by the number of social media impressions gained through user engagement, the responsiveness of email communications, and by the frequency and quality of content developed by the ER team, or successfully placed pitches to targeted media outlets. Social media impressions are an indicator of the quality of information and content XSEDE is generating and sharing with current communities, advanced computing enthusiasts, XSEDE users, and new communities. Effective use of social media tools and the generation of good content allows XSEDE to raise awareness of the value of advanced digital services. The number of science stories and announcements produced by media outlets are Area Metrics because they are an indicator of XSEDE’s ability to identify science success stories and to effectively work with targeted media in order to reach key audiences.
PY6 IPR 1 Page 53 Monthly open and click-through rates of the external XSEDE newsletter, Impact, are important Area Metrics because they indicate engagement levels of users and supporters of the project.
Table 9-2: Area Metrics for External Relations Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Number of Social PY10 Deepen/Extend Media PY9 — Raise impressions awareness of PY8 the value of PY7 advanced digital 228,000 services (§3.1.4) PY6 52,200 /yr Number of PY10 Deepen/Extend media hits PY9 — Raise awareness of PY8 the value of PY7 advanced digital PY6 147/yr 32 services (§3.1.4) Number of PY10 Deepen/Extend science success PY9 — Raise stories and awareness of announcements PY8 the value of appearing in PY7 advanced digital media outlets PY6 62/yr 14 services (§3.1.4) Monthly open PY10 Deepen/Extend and click- PY9 — Raise through rates of awareness of XSEDE’s PY8 the value of newsletter PY7 advanced digital Open: Open: services (§3.1.4) 35%/qtr 34.4% PY6 Click- Click- through: through 20%/qtr : 10.9% NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. XSEDE External Relations is on pace for our yearly goals in social media impressions, science success stories and announcements, and the open rates in our newsletters. We will increase activities to produce media “hits” in the new quarter due to new science content that is in development and coverage surrounding XSEDE’s activities at SC16. We anticipate new efforts to implement multimedia internal and external newsletters will help improve reader engagement and as a result produce higher click-thru rates. Our 10.9 percent click-thru rate for this quarter is still relatively high compared to industry standards (often as low as 3-4 percent). 9.3. Project Management, Reporting, & Risk Management (WBS 2.6.3) Project Management, Reporting & Risk Management (PM&R) enables an effective virtual organization through application of project management principles, provides visibility to project progress, successes, and challenges, brings new ideas and management practices into the project and disseminates lessons learned in XSEDE to other virtual organizations. Communication is critical to success in this highly distributed virtual organization. The coordination provided by PM&R ensures timely delivery of status reports to NSF, full and timely consideration of project change requests (PCRs), and awareness and evaluation of risks.
PY6 IPR 1 Page 54 Table 9-3: Area Metrics for Project Management, Reporting, & Risk Management Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Variance, in days, PY10 Sustain — Operate between relevant PY9 an effective and report productive virtual submission and PY8 organization due date PY7 (§3.3.3) PY6 0/qtr NA1 Percentage of PY10 Sustain — Operate risks reviewed PY9 an effective and productive virtual PY8 organization PY7 (§3.3.3) PY6 95%/qtr 100% Average number PY10 Sustain — Operate of days to execute PY9 an effective and PCR productive virtual PY8 organization PY7 (§3.3.3) <30 PY6 calendar 4 days/qtr NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 There are no relevant report submissions in PY6Q1. IPRs are submitted in the quarter following the reporting period and thus, this variance will be reported in the subsequent quarter. We have met our targets for this quarter. 9.4. Business Operations (WBS 2.6.4) The Business Operations group, working closely with staff at the University of Illinois’ Grants and Contracts Office (GCO) and National Center for Supercomputing Applications’ (NCSA) Business Office, will handle budgetary issues, manage sub-awards and assure timely processing of sub-award amendments and invoices. Business Operations will measure its ability to manage the business relationships with all sub- award institutions through the processing cycle-time of sub-award amendments and invoices. Efficient processing of sub-award amendments and invoices is required to avoid possible delays in the overall execution of the project.
Table 9-4: Area Metrics for Business Operations
Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Processing PY10 Sustain — times for stages PY9 Operate an of processing effective and sub-award PY8 productive amendments PY7 virtual <41 organization PY6 calendar NA1 (§3.3.3) days/qtr Processing PY10 Sustain — times for stages PY9 Operate an effective and PY8
PY6 IPR 1 Page 55 Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported of processing PY7 productive invoices <45 virtual PY6 calendar NA1 organization days/qtr (§3.3.3) Total decisions PY10 Sustain — and subsequent PY9 Operate an actions for effective and business PY8 productive practice and/or PY7 virtual project organization PY6 5/qtr 17 improvement (§3.3.3) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 Sub-award institutions do not have XSEDE2 contracts in place yet, so no amendments or invoices can be charged against the grant yet. The metrics regarding processing time for sub-award amendments and invoices will become relevant in the second quarter. The high number of Business Operations decisions and project improvements reflect the implementation of XSEDE1 lessons learned and feedback from the XSEDE sub-award partners. The actuals for each of the first three quarter are expected to exceed the target due to the increased efforts to improve the efficiency and effectiveness of the Business Operations responsibilities. Targets will be re-evaluated for PY7 as these are new metrics in use in XSEDE. 9.5. Strategic Planning, Policy & Evaluation (WBS 2.6.5) XSEDE dedicates effort to project-wide strategic planning, policy development, evaluation and assessment, and organizational improvement in support of sustaining an effective and productive virtual organization. XSEDE has engaged an independent Evaluation Team designed to provide XSEDE with information to guide program improvement and assess the impact of XSEDE services. Evaluations are based on five primary data sources: (1) an Annual User Survey that will be part of the XSEDE annual report and program plan; (2) an Enhanced Longitudinal Study encompassing additional target groups (e.g., faculty, institutions, disciplines, etc.) and additional measures (e.g., publications, citations, research funding, promotion and tenure, etc.); (3) an Annual XSEDE Staff Climate Study; (4) XSEDE KPIs, Area Metrics, and Organizational Improvement efforts, including ensuring that procedures are in place to assess these data; and (5) Specialized Studies as contracted by Level 2 directors and the Program Office. The first Area metric was designed to address the extent to which evaluation recommendations are considered by project leadership. The metric intentionally does not measure “implementation” of recommendations due to the many factors that may impede implementation (e.g. timelines, funding, capacity, etc.). The evaluation team intends to track this metric with new tools currently being implemented in XSEDE, namely JIRA and Confluence. The team intends to upload evaluation reports to Confluence and copy recommendations to JIRA for tracking. Since this L3 area is also responsible for strategic planning and policy, the second Area Metric measures how well-prepared XSEDE staff feel to perform their jobs. This metric is obtained by staff self-report via the Annual XSEDE Staff Climate Study conducted by the external evaluation team. Staff responses to a set of Likert scale items measured on a scale of 1 (Strongly Disagree) to 5 (Strongly Agree) and relating to staff preparation are combined to determine a mean index score.
PY6 IPR 1 Page 56 To deliver the most advanced and useful digital services for researchers, XSEDE must constantly evolve. This makes continuous evaluation and improvement of processes and policies critical to the effectiveness of XSEDE as an organization; therefore, the project’s KPI for sustaining an effective virtual organization is the number of improvements made. Potential improvements consist of recommendations based on the staff climate survey, efforts by area managers to increase efficiency or quality, adaptations in response to an evolving project or improvements to overall execution through implementation of widely validated methodologies. Process improvements are tracked via a self-reporting method. All areas of the project are asked to submit the process improvements they have made to their areas on a quarterly basis. From this list, the improvements are then classified as being either strategic or innovative and proactive or reactive.
Table 9-5: Area Metrics Strategic Planning, Policy & Evaluation Program Sub-goal Area Metric Target Q1 Q2 Q3 Q4 Total Year Supported Percentage of PY10 Sustain — Operate recommendations PY9 an effective and addressed by productive virtual relevant project PY8 organization areas PY7 (§3.3.3) PY6 90%/qtr NA1 Average rating of PY10 Advance — staff regarding PY9 Enhance the Array how well-prepared of Technical they feel to PY8 Expertise and perform their jobs PY7 Support Services 4 of 5 (§3.2.2) PY6 3.70 /yr Number of PY10 Sustain — Operate strategic or PY9 an innovative innovative virtual organization improvements PY8 (§3.3.4) PY7 PY6 9/yr 3 Ratio of proactive PY10 Sustain — Operate to reactive PY9 an innovative improvements virtual organization PY8 (§3.3.4) PY7 PY6 3:1/yr 8:1 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 L2 Directors are currently responding to climate study recommendations; data will be available in IPR2. We are well on our way toward meeting the SP&E metrics. In 2015 the average rating of staff regarding how well-prepared they feel to perform their jobs was 3.66, this has improved to 3.70 in 2016. The high ratio of proactive to reactive improvements is attributed to the extra effort of making improvements as part of the transition from XSEDE1 to XSEDE2.
PY6 IPR 1 Page 57 10. Appendices 10.1. Glossary and List of Acronyms ACRONYM DESCRIPTION Notes A3M Allocations, Accounting & Account Management A&AM Accounting & Account Management A&D Architecture & Design ADR Architecture Design Review AL2S Advanced Layer 2 Service Enables Internet2 users to create point-to-point VLANs AMIE Account Management Information Exchange API Application Programming Interface Area Metric A quantifiable measure that is used to track and assess the status of a specific process. Area metrics can measure performance or operational status. Area metrics relating to performance can be used alone or in combinations as a key performance indicator (KPI) for the project. CB Campus Bridging Infrastructure to make XSEDE resources appear to be proximal to the researcher’s desktop
CC Campus Champion CEE Community Enhancement & Engagement co-Pi Co-Principal Investigator CRI Capability & Resource Integration CRM Customer Relationship Management CS&E Computational Science & Engineering DNS Domain Name Service DNSKEY Domain Name Service Key DNSSEC DNS Security DTS Data Transfer Services E&O Education and Outreach ECSS Extended Collaborative Service e-infrastructure The integration of networks, grids, data centers and collaborative environments, and are intended to include supporting operation centers, service registries, credential delegation services.
ER External Relations ESRT Extended Support for Research Teams ESSGW Extended Collaborative Support for Science Gateways ESTEO Extended Support for Training, Education, & Outreach FTE Full Technical Equivalent GAAMP General Automated Atomic Model Parameterization GFFS Globus Federated File System
PY6 IPR 1 Page 58 GridFTP Grid File Transfer Protocol HPC High Performance Computing HPCU HSM Hardware Security Models I2 Internet2 IC Industry Challenge IdM Identity Management INCA/Nagios an service monitoring tool IR Incident Reports JIRA an activity tracking tool KB KB documents KPI Key Performance Indicators - A metric or combination of metrics meant to measure performance in key areas of the program so that actions and decisions which move the metrics in the desired direction also move the program in the direction of the desired outcomes and goals. L2 WBS Level 2 L3 WBS Level 3 MFC Minority Faculty Council MS Microsoft MSI Minority Serving Institution NCAR National Center for Atmospheric Research NCSA National Center for Supercomputing Applications NICS National Institute of Computational Science NIP OPS-SEC Operations - Cybersecurity OSG Open Science Grid OTP One Time Password PEP Program Execution Plan perfSONAR PERFormance Service Oriented Network monitoring Architecture PI Principal Investigator PM Project Management PM&R Project Management, Reporting & Risk Management POPS PACI Online Proposal System this is no longer an acronym and POPS is just the name for the allocation submission system; being supplanted by XRAS
PSC Pittsburgh Supercomputing Center PY Program Year RAS Resource Allocations Service RACD Requirements Analysis & Capability Delivery RESTful Representational state transfer
PY6 IPR 1 Page 59 rocks roll an open source cluster distribution solution that simplifies the processes of deploying, managing, upgrading, and scaling high-performance parallel computing clusters.
RSIG RT Request Tracker Ticketing System SACNAS Society for Advancement of Chicanos and Native Americans SCxy Supercomputing Conference (e.g. SC16) SD&I Software Development & Integration SDIACT Software Development & Integration Activity SDSC San Diego Supercomputer Center SH2 SH2 Security Shodor A National Resource for Computational Science Education SP Service Provider STEM Science Technology Engineering Mathematics SysOps Systems Operations TACC Texas Advanced Computing Center TEOS Training, Education and Outreach Service TAS Technology Audit Service TeraGrid an e-Science grid computing infrastructure combining resources at eleven partner sites. UCCAN Use Case Canonical Use Case UCCB Campus Bridging Use Case UCDA Data Analytics Use Case UCDM Data Management Use Case UCF Federation and Interoperation Use Case UCFC First Connecting Instrumentation Use Case UCHPC High Performance Computing Use Case UCHTC High Throughput Computing Use Case UCSGW Science Gateway Use Case UCSW Scientific Workflow Use Case UCVIS Visualization Use Case UE User Engagement UII User Interfaces and Information URC Under-represented communities URCE Under-Represented Community Engagement UREP User Requirement Evaluation and Prioritization URM Under-Represented Minority US United States WBS Work Breakdown Structure Numerical code for each group within XSEDE
PY6 IPR 1 Page 60 XCDB XSEDE Central Database The XDCDB contains 24 schemas, notably the accounting, resource repository, portal, and AMIE databases. XDCDB XSEDE Central Database XCI XSEDE Community Infrastructure XOC XSEDE Operation Center XRAC XSEDE Resource Allocation Committee XRAS XSEDE Resource Allocations Service XSEDE eXtreme Science and Engineering Discovery Environment XSEDE CA XSEDE Certificate Authorities Entity responsible for certifying encryption keys for identity management XSEDE KDC XSEDE Kerberos XSEDE14 XSEDE Conference in 2014 XSEDEnet an XSEDE-only network XSP XSEDE Scholars Program XTED XSEDE Technology Evaluation Database XUP XSEDE User Portal The XSEDE web pages at http://xsede.org XWFS XSEDE Wide File System
10.2. Metrics 10.2.1. SP Resource and Service Usage Metrics To demonstrate its success and help focus management attention on areas in need of improvement, XSEDE monitors a wide range of metrics in support of different aspects of “success” for the program. The metrics presented in this section provides a view into XSEDE’s user community, including XSEDE’s success at expanding that community, the projects and allocations through which XSEDE manages access to resources, and the subsequent use of the resources by the community. Table 10-1 summarizes a few key measures of the user community, the projects and allocations, and resource utilization. Expanded information and five-year historical trends are shown in three corresponding subsections. In Q3 2016, the XSEDE user community continued to climb on many measures. The number of open user accounts neared 9,500; the number of gateway users remained above 4,000; more than 1,700 new user accounts were added to SP resources; and more than 420 institutions were represented among the users running jobs. The number of gateway users once again exceeded traditional users for the quarter. More details are in §10.2.1.1. Project and allocation activity held strong, with resource requests 3 times what was available; the XRAC recommended support for 1.5 times what was available. More details are in §10.2.1.2. XSEDE resource metrics rebounded in several areas. Total capacity decreased slightly to 16.0 Pflops (peak) with the retirements of the Mason system at Indiana U and the Darter system at NICS. The central accounting system showed 12 compute resources reporting activity, with Jetstream, Bridges, and Bridges-Large entering full production and sending accounting records.
PY6 IPR 1 Page 61 Altogether, SP resources delivering 36.2 billion NUs of computing, 14% more than the previous quarter. More details are in §10.2.1.3. Table 10-1: Quarterly activity summary
User Community Q4 2015 Q1 2016 Q2 2016 Q3 2016 Open user accounts 8,119 8,539 9,295 9,442 Active individuals 2,606 2,883 3,256 2,810 Gateway users 4,121 4,040 4,355 4,125 New user accounts 835 1,382 1,279 1,714 Active fields of science 39 38 36 39 Active institutions 393 390 387 422 Projects and Allocations NUs available at XRAC 39.1B 37.9B 44.4B 44.0B NUs requested at XRAC 116.4B 94.7B 132.9B 129.3B NUs recommended by XRAC 55.2B 44.8B 63.0B 65.3B NUs awarded at XRAC 38.4B 36.2B 42.5B 41.7B Open projects 1,862 1,964 1,744 2,145 Active projects 1,105 1,964 1,220 1,329 Active gateways 16 15 16 16 New projects 227 217 278 320 Closed projects 255 239 250 281 Resources and Usage Resources open (all types) 25 26 26 33 Total peak petaflops 13.3 14.3 16.2 16.0 Resources reporting use 10 8 10 12 Jobs reported 0.75M 0.962M 1.36M 1.62M NUs delivered 33.4B 32.0B 31.8B 36.2B
10.2.1.1. User community metrics Figure 2 shows the five-year trend in the XSEDE user community, including open user accounts, total active XSEDE users, active individual accounts, active gateway users, the number of new HPC user accounts, and the total number of new XUP accounts at the end of each quarter. Portal- only user accounts can be used for training course registration and other non-HPC services. The quarter saw 9,442 open accounts but a decrease to 2,810 users charging jobs. (The reason for the decrease is unclear, but fewer users were reported from at least seven different resources.) The number of active gateway users reported dropped to 3,310, but continued to surpass the number of active traditional users. Figure 3 shows the activity on XSEDE resources according to field of science across program years, including the relative fraction of PIs, open accounts, active users, allocations, and NUs used according to discipline. The figure shows the fields of science that consume ~2% or more of delivered NUs per quarter. PIs and users are counted more than once if they are associated with projects in different fields of science. The quarterly data show that the percentages of PIs and accounts associated with the “other” disciplines represent almost 30% of all PIs, more than 40% of user accounts, and 25% of active users. Collectively the “other” fields of science represented 8% of total quarterly usage.
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Figure 2. XSEDE user census, excluding XSEDE staff.
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Figure 3. Quarterly XSEDE user, allocation, and usage summary by field of science, in order by usage, excluding staff projects. Note: PIs and users may appear under more than one field of science.
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Table 10-2 and Table 10-3 highlight aspects of the broader impact of XSEDE. The former shows that graduate students, postdoctoral researchers, and undergraduates consistently make up 65% of the XSEDE user base. The latter table shows XSEDE’s reach into targeted institutional communities. Institutions with Campus Champions represent a large portion of usage because this table shows all users at Campus Champion institutions, not just those on the champion’s project. The table also shows XSEDE’s reach into EPSCoR states, the MSI community, and countries outside the U.S. The table reflects increases to MSI community metrics based on a review of MSI status for institutions in the XDCDB that correctly noted the MSI status for 45 additional institutions.
Table 10-2: End of quarter XSEDE open user accounts by type, excluding XSEDE staff.
Category Q4 2015 Q1 2016 Q2 2016 Q3 2016 Graduate Student 3,318 3,567 3,945 3,977 Faculty 1,581 1,595 1,639 1,691 Postdoctoral 1,144 1,165 1,260 1,268 Undergraduate Student 866 948 1,078 966 University Research Staff (excluding postdocs) 560 583 609 634 High school 41 42 25 92 Others 608 637 736 814 TOTALS 8,118 8,537 9,292 9,442
Table 10-3: Active institutions in selected categories. Institutions may be in more than one category. Category PY4 Q4 PY5 Q1 PY5 Q2 PY5 Q3 Campus Sites 89 86 85 90 Champions Users 1,229 1,269 1,318 1,355 % total NUs 51% 47% 47% 59% EPSCoR Sites 73 71 75 74 states Users 369 418 457 372 % total NUs 15% 13% 17% 11% MSIs Sites 19 35 31 33 Users 71 164 198 223 % total NUs 0.2% 2.9% 3.3% 3.2% International Sites 54 44 50 50 Users 85 60 77 107 % total NUs 2% 1% 1% 1% Total Sites 390 387 392 422 Users 2,489 2,817 2,919 2,810
10.2.1.2. Project and allocation metrics Figure 4 shows the five-year trend for requests and awards at XSEDE quarterly allocation meetings. The figure shows a slight decrease in demand for Q3 2016, consistent with slightly fewer requests received, and allocations levels remained roughly flat. NUs requested were 3x greater than NUs available, and XRAC recommendations were 1.5x more than NUs available.
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Figure 4. Five-year allocation history, showing NUs requested, awarded, available, and recommended. Table 10-4 presents a summary of overall project activity, and Table 10-5 shows projects and activity in key project categories as reflected in allocation board type. Note that Science Gateways may appear under any board. In Q3 2016, the number of open and active projects (those making use of resources) climbed, surpassing the peaks of two quarters before. As a special class of projects, science gateway activity is detailed in Figure 5, showing continued high levels of usage and users from these projects. Table 10-4: Project summary metrics Project metric Q4 2015 Q1 2016 Q2 2016 Q3 2016 XRAC requests 256 202 234 205 XRAC request success 75% 74% 71% 76% XRAC new awards 57 47 63 60 Startups requested 168 217 221 243 Startups approved 128 171 181 232 Projects new 217 278 296 320 Projects closed 239 250 242 281
PY6 IPR 1 Page 66 Table 10-5: Project activity by allocation board type
Q4 2015 Q1 2016 Q2 2016 Q3 2016
Open Active % Open Active % Open Active % Open Active % projects projects NUs projects projects NUs projects projects NUs projects projects NUs
Campus 138 57 0.3% 134 60 0.2% 120 65 0.4% 144 55 0.3% Champions
Discretionary 0 0 0.0% 0 0 0.0% 0 0 0.0% 3 2 0.0%
Educational 81 43 0.3% 93 45 0.2% 78 47 0.2% 109 56 0.4%
Staff 25 11 0.1% 25 10 0.0% 27 11 0.1% 31 17 0.1%
Startup 900 378 2.4% 972 434 3.1% 817 451 3.2% 1,077 517 3.7%
XRAC 718 616 96.9% 740 625 96.5% 702 646 96.0% 781 682 95.5%
Totals 1,862 1,105 100% 1,964 1,174 100% 1,744 1,220 100% 2,145 1,329 100%
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Figure 5. Quarterly gateway usage (NUs), jobs submitted, users (reported by ECSS), registered gateways, and active gateways.
10.2.1.3. Resource and usage metrics SP systems delivered 36.2 billion NUs in Q3 2016, up 14% from the previous quarter, and up 17% from the year-ago quarter. Table 10-6 breaks out the resource activity according to different resource types. Jetstream became the first cloud system to report usage in Q3 2016. Figure 6 shows the total NUs delivered by XSEDE-allocated SP computing systems, as reported to the central accounting system over the past five years.
PY6 IPR 1 Page 68 Table 10-6: Resource activity, by type of resource, excluding staff projects. Note: A user will be counted for each type of resource used.
Q4 2015 Q1 2016 Q2 2016 Q3 2016
High-performance Resources 4 4 6 6 computing Jobs 642,272 867,199 1,260,052 1,505,559
Users 2,247 2,628 2,734 2,548
NUs 30,100,607,790 28,927,209,855 28,940,148,911 32,693,224,763
Data-intensive Resources 1 1 2 3 computing Jobs 86,479 70,558 84,134 66,719
Users 335 280 319 319
NUs 2,636,254,048 2,467,503,196 2,663,052,850 2,364,613,248
High-throughput Resources 1 1 1 computing
Jobs 4,140 6,890 6,581
Users 14 22 15
NUs 488,933,995 444,493,931 804,701,012
Visualization system Resources 1 1 1 1
Jobs 8,767 10,854 8,117 7,474
Users 61 98 72 91
NUs 130,973,305 151,525,189 133,334,396 240,341,685
Cloud system Resources - - - 1
Jobs - - - 32,081
Users - - - 47
NUs - - - 74,404,041
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Figure 6. Total XSEDE resource usage in NUs.
10.2.1.4. Data Services XSEDE supports monitoring for the Globus data transfer service for connecting XSEDE service providers and external sites. Table 10-7 shows summary metrics and increasing Globus adoption over the past three years. Figure 7 shows the trends in Globus data transfer activity from the start of the XSEDE program, with the high points labeled for each metric.
PY6 IPR 1 Page 70 Table 10-7: Globus Online activity to and from XSEDE endpoints, excluding GO XSEDE speed page user.
Q4 2015 Q1 2016 Q2 2016 Q3 2016
Files to XSEDE (millions) 35 120 116 108
TB to XSEDE 1,710 1,361 1,407 1,394 To/from XSEDE Files from XSEDE (millions) 200 221 202 131 endpoint TB from XSEDE 1,795 1,804 1,263 2,626
Users 489 603 601 493
Files to XSEDE (millions) 5 5 7 16
TB to XSEDE 65 75 50 79 To/from XSEDE Files from XSEDE (millions) 30 73 16 31 via Globus Connect TB from XSEDE 184 227 82 83
Users 347 435 417 345
TB to XSEDE 322 216 219 346
To/from TB from XSEDE 280 330 386 492 XSEDE from/to Campuses 26 35 39 36 Campuses
Campus endpoints 38 46 59 44
TB to Campuses 7,077 6364 6959 11,346
TB from Campuses 7,423 7,583 7,544 11,556 To/from Campus Campuses 101 102 111 117
Campus endpoints 275 303 332 329
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Figure 7. Top: Aggregate Globus file and data transfer activity to and from XSEDE endpoints. Bottom: Numbers of active Globus users and campuses moving data to and from XSEDE endpoints.
PY6 IPR 1 Page 72 10.2.2. Other Metrics 10.2.2.1. Community Engagement & Enrichment (WBS 2.1) (Gaither) PY6Q1 TPY6Q2 PY6Q3 PY6Q4 Total Metric a r Number of new users of XSEDE resources > 1,000 1881 g and services (Area Metric) e Number of sustained users of XSEDE t > 5,000 4755 resources and services (Area Metric) Number of new users from underrepresented communities using > 100 150 XSEDE resources and services (Area Metric) Number of sustained users from underrepresented communities using XSEDE resources and services > 1,000 322 (KPI) Number of attendees in synchronous and 1304 > 5,000 asynchronous training (Area Metric) Average impact assessment of training for 4.54 attendees registered through XSEDE User 4 of 5 Portal (Area Metric) Number of pageviews to the XSEDE website 49,409 80,000 (Area Metric) Number of pageviews to the XSEDE User 183408 100,000 Portal (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.1.1. Workforce Development (WBS 2.1.2) (Lathrop) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of unique attendees, 1,600 112 synchronous training (Area Metric) Number of total attendees, synchronous training 2,000 188 (One person can take several classes) (Area Metric) Number of unique attendees, asynchronous training (Area Metric) 2,000 215
Number of total attendees, asynchronous training (One person can take several 4,000 1116 classes) (Area Metric) Average impact assessment of training for attendees registered through XSEDE 4 of 5 4.54 User Portal (Area Metric) Number of formal degree, minor, and certificate programs added to the 3 1 curricula (Area Metric) Number of materials contributed to 40 10 public repository (Area Metric)
PY6 IPR 1 Page 73 Number of materials downloaded from 56,000 11,114 the repository (Area Metric) Number of computational science 40 - modules added to courses (Area Metric) Number of students benefitting from 50 276 XSEDE resources and services Percentage of under-represented students benefitting from XSEDE 42% resources and services NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. - Data reported annually. 10.2.2.1.2. User Engagement (WBS 2.1.3) (Hempel) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of active and new PIs contacted All Active 1533 quarterly (Area Metric) PIs Number of user requirements All issues 32 entered/tracked (Area Metric) identified through regular contact with PIs Number of user requirements resolved 16 (Area Metric) Number of responses to PI emails each 118 quarter Number of responses to each micro survey * Number of annual user satisfaction survey ** respondents interviewed Number of XSEDE-wide tickets 7 Number of XSEDE-wide tickets addressed 6 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. * Documentation micro survey in progress ** Interview planning in progress 10.2.2.1.3. Broadening Participation (WBS 2.1.4) (Akli) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of new under-represented individuals using XSEDE resources and >100 150 services (Area Metric) Number of sustained under-represented individuals using XSEDE resources and >1000 322 services (Area Metric) Longitudinal Assessment of Inclusion in 5% XSEDE via the Staff Climate Study (Area improve- - Metric) ment Longitudinal Assessment of Equity in 5% XSEDE via the Staff Climate Study (Area improve- - Metric) ment
PY6 IPR 1 Page 74 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.1.4. User Interfaces & Online Information (WBS 2.1.5) (Dahan) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of new users of XSEDE resources 5,000 1,881 and services (Area Metric) Number of sustained users of XSEDE 4755 8,000 resources and services (Area Metric) Number of pageviews to the XSEDE 49,409 80,000 website (Area Metric) Number of pageviews to the XSEDE User 183,408 100,000 Portal (Area Metric) User satisfaction with website (Area - 4 of 5 Metric) User satisfaction with user portal (Area - 4 of 5 Metric) User satisfaction with user - 4 of 5 documentation (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.1.5. Campus Engagement (WBS 2.1.6) (Neeman, Brunson) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of Institutions with a Champion 225 (for all 224 (Area Metric) of PY6) Number of unique contributors to the Champion email list 50 90 ([email protected]) (Area Metric) Number of activities that (i) expand the emerging CI workforce and/or (ii) 10 improve the extant CI workforce, 20 participated in by members of the Campus Engagement team (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.2. Extended Collaborative Support Services (WBS 2.2) (Wilkins-Diehr, Roskies) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of completed ECSS projects 50 10 (ESRT + ESCC + ESSGW) (KPI) Average ECSS impact rating (KPI) 4 of 5 4.56 Average satisfaction with ECSS support 4.5 of 5 4.86 (KPI) Number of new users from non- traditional disciplines of XSEDE 100 147 resources and services (KPI) Number of sustained users from non- traditional disciplines of XSEDE 100 451 resources and services (KPI) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 75 10.2.2.2.1. Extended Support for Research Teams (WBS 2.2.2) (Crosby) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of completed ESRT projects 30 3 (KPI) Average ECSS impact rating (KPI) 4 of 5 5 Average satisfaction with ECSS support 4.5 of 5 5 (KPI) Number of Projects Initiated 4 Number of Projects Discontinued 0 Number of PI interviews 2 Number of Active Projects 32 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.2.2. Novel and Innovative Projects (WBS 2.2.3) (Sanielevici) Metrics Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of new users from non- traditional disciplines of XSEDE 100 147 resources and services (KPI) Number of sustained users from non- traditional disciplines of XSEDE 100 451 resources and services (KPI) Number of new XSEDE projects from target communities generated by NIP 20 16 (Area Metric) Number of successful XSEDE projects from target communities mentored by 10 23 NIP (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
10.2.2.2.3. Extended Support for Community Codes (WBS 2.2.4) (Cazes) Metrics Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of completed ESCC projects 10 3 (KPI) Average ECSS impact rating (KPI) 4 of 5 4.67 Average satisfaction with ECSS support 4.5 of 5 4 (KPI) Number of projects initiated 6 Number of projects discontinued 0 Number of active projects 11 Number of PI Interviews 9 5 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 76 10.2.2.2.4. Extended Support for Science Gateways (WBS 2.2.5) (Pierce) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of completed ESSGW projects 10 4 (KPI) Average ESSGW impact rating (KPI) 4 of 5 5 Average satisfaction with ESSGW support 4.5 of 5 5 (KPI) Number of unique gateway users per 3000 2694 quarter (Area Metric) Number of projects initiated 1 Number of projects discontinued 0 Number of active projects 12 Number of PI Interviews 2 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
10.2.2.2.5. Extended Support for Education Outreach, & Training (WBS 2.2.6) (Alameda) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of Campus Champions fellows 4 5 (Area Metric) Average Score of fellows assessment 4 of 5 4.33 (Area Metric) Number of live training events staffed 20 7 (Area Metric) Number of staff training events (Area 2 0 Metric) Attendees at staff training events (Area 40 0 Metric)
Attendees at ECSS Symposia (Area 300 78 Metric) Requests for Service 5 Training Modules Reviewed 1 Training Modules Produced 5 Meetings and BoFs 9 Mentoring 8 Talks and Presentations 8 Education Proposals reviewed 21 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.3. Community Infrastructure (WBS 2.3) (Lifka) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total
PY6 IPR 1 Page 77 Average satisfaction rating of XCI 4 of 5 services (Area Metric) Number of new capabilities made available for production deployment 7 9 (Area Metric) Number of capabilities delivered/ 1 Number planned (Area Metric) Total number of systems that use one or 450 512 more CRI provided toolkits (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.3.1. Requirements Analysis & Capability Delivery (WBS 2.3.2) (Navarro) PY6Q PY6Q PY6Q PY6Q Total Metric Target 1 2 3 4 Number of capability delivery plans prepared for prioritized Use Cases (Area 7 9 Metric) Number of CI integration assistance 6 4 engagements (Area Metric) User rating of components delivered in 4 of 5 5 production (Area Metric) Operator rating of components delivered for production deployment 4 of 5 5 (Area Metric) Software/service provider rating of our 4 of 5 4.5 integration assistance (Area Metric) Responsiveness to defect and support 45 days or 7 days requests (Area Metric) less NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.3.2. XSEDE Capability & Resource Integration (WBS 2.3.3) (Knepper) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Total number of systems that use one or more CRI provided toolkits (Area 450 512 Metric) Number of Level 1 total systems that fully incorporate all of the 5 recommended tools from the XSEDE 6/6 Community Repository (Area Metric) Number of repository subscribers to SCRI cluster and laptop toolkits (Area 150 91 Metric) Aggregate number of TeraFLOPS of cluster systems using SCRI toolkits 1000 100 (Area Metric) Number of partnership interactions between XCRI and SPs, national CI 12 organizations, and campus CI providers 3 (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
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10.2.2.4. XSEDE Operations (WBS 2.4) (Peterson) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Average composite availability of core services (geometric mean of critical 99% 99.9 services and XRAS) (Area Metric) Hours of downtime with direct user impacts from an XSEDE security <24 0 incident. (Area Metric) Mean time to ticket resolution by XOC and WBS ticket queues (hrs) (Area <24 24.0 Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.4.1. Cybersecurity (WBS 2.4.2) (Slagell, Marstellar)
Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Hours of downtime with direct user impacts from an XSEDE security <24 0 incident. (Area Metric) Hours of downtime WITHOUT direct user impacts from an XSEDE (affects central service or multiple SPs) < 24 0 security incident. XSEDE account exposures < 10 0 Time to disable XSEDE accounts exceeding 24 hours. 0 0 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.4.2. Data Transfer Services (WBS 2.4.3) (Boerner) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Performance (Gbps) of instrumented, intra-XSEDE transfers > 1GB (Area 1 Gbps 1.6 Metric) New services added 0 Services retired 0 Total Globus Online users 411 Total new Globus Online users 111 Total transfers (Million) inbound 48 Total transfers (Million) outbound 772 Size of transfers (TBs) inbound 999 Size of transfers (TBs) outbound 1,561 Total number of days in which any Network Interface error occurred 0 XSEDEnet maximum bandwidth used (Gbps) 11.7 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016.
PY6 IPR 1 Page 79 10.2.2.4.3. XSEDE Operations Center (WBS 2.4.4) (Pingleton) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Mean time to resolution in XOC queue 4.2 <24 (Area Metric) User satisfaction with tickets closed by 4.8 4 of 5 the XOC (Area Metric) Mean time to resolution in WBS queue 72.5 Number of Support tickets opened for 218 WBS queues Number of Support tickets closed by 187 WBS queues Number of Support tickets opened for 444 XOC Number of Support tickets closed by 442 XOC Mean time to first response by XOC < 24 hrs 0.44 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.4.4. System Operations Support (WBS 2.4.5) (Rogers) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Average availability of critical 99.9 enterprise services (%) [geometric 99% mean] (Area Metric) Total enterprise services 47 Core enterprise services 8 Services added 0 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.5. Resource Allocation Service (WBS 2.5) (Hart) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Average user satisfaction rating for allocations and other support services 4 of 5 3.98 (KPI) Availability of XRAS (Area Metric) 99% 99.9% NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.5.1. XSEDE Allocations Process & Policies (WBS 2.5.2.) (Hackworth) PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Metric Target
User satisfaction with allocations process 3.97 4 of 5 (Area Metric) Average time to process Startup requests 2 weeks or 10.65 (Area Metric) less days Percentage of XRAC-recommended SUs 62% 100% allocated (Area Metric) Continuous allocation requests 347 processed Research allocation requests processed 354
PY6 IPR 1 Page 80 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.5.2. Allocations, Accounting, & Account Management CI (WBS 2.5.3) (Schuele) PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Metric Target
User satisfaction with XRAS system (Area 3.98 4 of 5 Metric) Availability of the XRAS systems (Area 99.9% 99% Metric) Percentage of approved feature change 100% 100% requests implemented (Area Metric) Number of XRAC client organizations 4 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.6. Program Office (WBS 2.6) (Payne) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total
Number of Social Media impressions (Area 190,000 Metric) Number of media hits (Area Metric) 140 Percentage of recommendations addressed by relevant project areas (Area 90% Metric) Number of strategic or innovative 9 improvements (Area Metric) Ratio of proactive to reactive 3 improvements (Area Metric) Number of staff publications (Area Metric) 70 5 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.6.1. External Relations (WBS 2.6.2) (Williamson) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Number of Social Media impressions 228,000 52,200 (Area Metric) Number of media hits (Area Metric) 147 32 Number of science success stories and announcements appearing in media 62 14 outlets (Area Metric) Monthly open and click-through rates of Open: Open: XSEDE’s newsletter (Area Metric) 35% 34.4% Click- Click- through: through: 20% 10.9% NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.6.2. Project Management, Reporting, & Risk Management (WBS 2.6.3) (Gendler) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total
PY6 IPR 1 Page 81 Variance, in days, between relevant NA1 report submission and due date (Area 0 Metric) Percentage of risks reviewed (Area 100 95% Metric) Average number of days to execute PCR 30 calendar 4 (Area Metric) days Number of total risks 136 Number of active risks 123 Number of new risks 0 Number of triggered risks 0 Number of modified risks 0 Number of retired risks 13 Number of PCRs submitted 6 KPI/Metrics 5 Technical 0 Scope 0 Budget 1 Staff 0 Other 0 Number of PCRs in progress 1 Number of PCRs approved 4 Number of PCRs rejected 1 Number of PCRs resolved 5 Number of PCRs annulled 0 NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 1 There are no relevant report submissions in PY6Q1. IPRs are submitted in the quarter following the reporting period and thus, this variance will be reported in the subsequent quarter. 10.2.2.6.3. Business Operations (WBS 2.6.4) (Payne) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total Processing times for stages of processing 41 calendar sub-award amendments (Area Metric) days Processing times for stages of processing 45 calendar invoices (Area Metric) days Total decisions and subsequent actions for 5 per business practice and/or project quarter improvement (Area Metric) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.2.2.6.4. Strategic Planning, Policy, & Evaluation (WBS 2.6.5) (Payne) Metric Target PY6Q1 PY6Q2 PY6Q3 PY6Q4 Total
PY6 IPR 1 Page 82 Percentage of recommendations addressed 90% by relevant project areas (Area Metric) Average rating of staff regarding how well- prepared they feel to perform their jobs 4 of 5 (Area Metric) Number of strategic or innovative 9 improvements (Area Metric) Ratio of proactive to reactive improvements 3 (Area Metrics) NOTE: PY6 is unique and only spans September 2016-April 2017. PY6 Q1 includes September-October 2016. 10.3. Scientific Impact Metrics (SIM) and Publications Listing This appendix presents the current Scientific Impact Metrics data as of end of September 2016. This is part of the XD Metrics Service (XMS) (previously NSF Technology Audit Service (TAS)) effort. We are currently working on adapting the data and results structure so most of these results would be available as display via XDMoD in future. 10.3.1. Summary Impact Metrics Table SIM-1 shows the essential scientific summary impact metrics as of the end of Q3 2016. The increasing values for each metric are listed in the table indicating the changes during the last quarter. By calculating such metrics periodically, we can show the trends, as depicted in Figure SIM-2 and Figure SIM-3. Both show steadily increasing trends. Table SIM-1: Overall Scientific Impact Metrics Data
Number of i10-index (Number of externally verified Overall publications cited at h-index g-index unique citation count* least 10 times) publications*
Since 2005 9,505 3,805 181,148 148 258 (TG+XD) Since 2011 7,320 2,216 80,368 85 144 (XD) Change from +843 +324 +14,162 +7 +5 last quarter (TG+XD) Change from +765 +268 +10,753 +4 +9 last quarter (XD) * Data updated as of September 30th, 2016.
10.3.2. Historical Trend Figure SIM-2 and Figure SIM-3 show the increasing monthly trend regarding publications, citations, and other impact metrics such as H-Index and G-Index. Both suggest the increasing impact of XSEDE during the past year, based on verified unique publication count; citation count; h-index and g-index. Note that either h-index or g-index is hard to increase significantly from a
PY6 IPR 1 Page 83 value that was already very high, due to the intrinsic definition and calculation method, but we still observe improvements.
Figure SIM-2. Counts of all externally verified publications for TG/XD (since 2005) and XD (since 2011) and of those being cited at least 10 times (i10-index). Note the hike in the beginning of each quarter (October, January, April, July) due to the publication submission for the quarterly XRAC meeting review.
Figure SIM-3. Accumulated citation count (line, left axis) as well as h-index and g-index metrics (bar, right axis) for TG/XD (since 2005) and XD (since 2011).
10.3.3. Publications Listing Figure 4 shows the number of publications, conference papers, and presentations reported by XSEDE users each quarter, including the 923 reported by 204 projects in PY5 Q3; Appendix D lists these publications according to allocated project. Starting with PY4 Q2 (2014 Q4), we began transitioning to counting articles submitted to XSEDE’s publications database, augmented by manual extraction from files submitted to XRAS. The transition reduced the number of publication due to more focused data entry by users (i.e., fewer non-scholarly articles, invited talks/seminars, and so on), fewer duplicate entries, and a generally cleaner data set.
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Figure 4. Publications, conference papers, and presentations reported by XSEDE users. 10.3.3.1. XSEDE Staff Publications The following staff publications were reported in 2016 Q3, published in 2013-2016, and reported via the XSEDE User Portal user profiles. 1. Akli, L., J. Alameda, S. I. Gordon, M. Madrid, and L. Rivera (2016), XSEDE Training, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949586. (published) [ECSS, Training] 2. Darris, C. E., J. E. Tyus, G. Kelley, A. J. Ropelewski, H. B. Nicholas, X. Wang, and S. Nahashon (2015), Molecular tools to support metabolic and immune function research in the Guinea Fowl (Numida meleagris), BMC Genomics, 16(1), doi:10.1186/s12864-015-1520-6. (published) [Blacklight, NIP, PSC, Training] 3. Martínez, T., A. J. Ropelewski, R. González-Mendez, G. J. Vázquez, and I. E. Robledo (2016), Draft Genome Sequence of a Multidrug-ResistantKlebsiella pneumoniaeCarbapenemase-ProducingAcinetobacter baumanniiSequence Type 2 Isolate from Puerto Rico, Genome Announc., 4(4), e00758–16, doi:10.1128/genomea.00758-16. (published) 4. Mendez, R. G., J. Torres, P. Ishwad, H. B. Nicholas, and A. Ropelewski (2016), Assisting Bioinformatics Programs at Minority Institutions, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949641. (published) [Blacklight, Data Supercell, PSC] 5. Scarborough, W., C. Arnold, and M. Dahan (2016), Case Study, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949655. (published) [Science Gateways, TACC]
10.3.3.2. Publications from XSEDE Users Most of the following publications were gathered from Research submissions to the August 2016 XSEDE Resource Allocations Committee (XRAC) meeting. We are continuing to transition from having XRAC renewal submissions provide a file specifically to identify publications resulting from the work conducted in the prior year to requiring submissions to add publications to their
PY6 IPR 1 Page 85 user profiles in the XSEDE User Portal during the proposal submission via XRAS. The publications are organized by the proposal with which they were associated. This quarter, 212 projects identified 1,363 publications and other products that were published, in press, accepted, submitted, or in preparation. In PY4 of the XSEDE program, we began transitioning to counting articles submitted to XSEDE’s publications database, augmented by manual extraction from files submitted to XRAS. The transition reduced the number of publication due to more focused data entry by users (i.e., fewer non-scholarly articles, invited talks/seminars, and so on), fewer duplicate entries, and a generally cleaner data set. This quarter fewer than 25% of XRAC submissions included separate files; all other projects added directly to the database. Also note that the final set of publications are associated with “XSEDE Allocation Request” and not a specific project; we will be working to encourage users to associate such publications with the proper XSEDE project. 1. TG-ASC040044N 1. Aikens, K., Dhamankar, N., Blaisdell, G., Lyrintzis, A. 2015. An Efficient Large Eddy Simulation (LES) Methodology for Jet Aeroacoustics-Recent Developments. INASE (Zakynthos, Greece). (published) [Stampede, TACC] 2. Dhamankar, N. 2016. An Immersed Boundary Method for Efficient Computational Studies of Nozzles Designed to Reduce Jet Noise. Ph.D. thesis. Purdue University. (published) [Stampede, TACC] 3. Dhamankar, N., Blaisdell, G., Lyrintzis, A. 2016. Analysis of Turbulent Jet Flow and Associated Noise with Round and Chevron Nozzles using Large Eddy Simulation. AIAA/CEAS Aeroacoustics Conference (Lyon, France). (published) [Stampede, TACC] 4. Dhamankar, N., Blaisdell, G., Lyrintzis, A. 2016. A simple extension of digital filter-based turbulent inflow to non-uniform grids. Aerospace Science and Technology. (accepted) [Stampede, TACC] 2. TG-ASC040046 5. K. Aikens, N. Dhamankar, G. Blaisdell, and A. Lyrintzis, “An Efficient Large Eddy Simulation (LES) Methodology for Jet Aeroacoustics-Recent Developments,” INASE, Zakynthos, Greece, July, 2015. 6. Nitin S. Dhamankar, “An Immersed Boundary Method for Efficient Computational Studies of Nozzles Designed to Reduce Jet Noise,” Ph.D. Thesis, School of Aeronautics and Astronautics, Purdue University, May 2016. 7. Nitin S. Dhamankar, Gregory A. Blaisdell, Anastasios S. Lyrintzis, “Analysis of Turbulent Jet Flow and Associated Noise with Round and Chevron Nozzles using Large Eddy Simulation,” AIAA Paper 2016-3045, 22nd AIAA/CEAS Aeroacoustics Conference, May 30 – June 1, 2016, Lyon, France. 8. N. S. Dhamankar, G. A. Blaisdell, A. S. Lyrintzis, “A simple extension of digital filter-based turbulent inflow to non-uniform structured grids,” Aerospace Science and Technology, to appear (accepted 6/28/2016). 3. TG-ASC120007 9. Kumar, A., and S. Balasubramanian (2015), Sparse representation based background subtraction in videos, 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), doi:10.1109/ncvpripg.2015.7489942. (published) [Stampede, TACC] 4. TG-ASC140011 10. Moncada, H., Moore, S., Rumpf, R. 2016. Towards Parallelization and Scalability of the Spatially Variant Lattice Algorithm. Efficient implementation of a computational electromagnetics algorithm to generate spatially variant lattices. XSEDE16. https://www.xsede.org/web/xsede16/posters. (published) 5. TG-ASC140026 11. Ardekani, S., Jain, S., Sanzi, A., Corona-Villalobos, C., Abraham, T., et al. 2016. Shape analysis of hypertrophic and hypertensive heart disease using MRI-based 3D surface models of left ventricular geometry. Medical image analysis, vol. 29, pp. 12-23, 2016. (published) [Gordon, SDSC, Stampede, TACC] 12. Faria, A., Ratnanather, J., Tward, D., Lee, D., Van Den Noort, F., et al. 2016. Linking white matter and deep gray matter alterations in premanifest Huntington disease. NeuroImage: Clinical 11: 450--460. (published) [Gordon, Science Gateways, SDSC, Stampede, TACC]
PY6 IPR 1 Page 86 13. Mori, S., D. Wu, C. Ceritoglu, Y. Li, A. Kolasny, M. A. Vaillant, A. V. Faria, K. Oishi, and M. I. Miller (2016), MRICloud: Delivering High-Throughput MRI Neuroinformatics as Cloud-Based Software as a Service, Computing in Science & Engineering, 18(5), 21–35, doi:10.1109/mcse.2016.93. (published) [Gordon, Science Gateways, SDSC, Stampede, TACC] 14. Tang, X., Qin, Y., Wu, J., Zhang, M., Zhu, W., et al. 2016. Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease. Magnetic resonance imaging 34: 1087-- 1099. (published) [Gordon, SDSC, Stampede, TACC] 15. Varma, V., Tang, X., Carlson, M. 2016. Hippocampal sub-regional shape and physical activity in older adults. Hippocampus. (published) [Gordon, Science Gateways, SDSC, Stampede, TACC] 16. Wu, D., Ma, T., Ceritoglu, C., Li, Y., Chotiyanonta, J., et al. 2016. Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. NeuroImage 125: 120--130. (published) [Gordon, Science Gateways, SDSC, Stampede, TACC] 17. A. V. Faria, J. T. Ratnanather, D. J. Tward, D. S. Lee, F. Van Den Noort, D. Wu, T. Brown,H. Johnson, J. S. Paulsen, C. A. Ross, et al., “Linking white matter and deep gray matter alterations in premanifest huntington disease," NeuroImage: Clinical, vol. 11, pp. 450-460, 2016. 18. X. Tang, Y. Qin, J. Wu, M. Zhang, W. Zhu, and M. I. Miller, “Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in alzheimer's disease," Magnetic resonance imaging, vol. 34, no. 8, pp. 1087-1099, 2016. 19. D. Wu, T. Ma, C. Ceritoglu, Y. Li, J. Chotiyanonta, Z. Hou, J. Hsu, X. Xu, T. Brown, M. I. Miller, et al., “Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on t1-weighted mri," NeuroImage, vol. 125, pp. 120-130, 2016. 20. A. V. Faria, K. Oishi, S. Yoshida, A. Hillis, M. I. Miller, and S. Mori, “Content-based image retrieval for brain mri: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis," NeuroImage: Clinical, vol. 7, pp. 367-376, 2015. 21. M. I. Miller, A. V. Faria, K. Oishi, and S. Mori, “High-throughput neuro-imaging informatics," Recent Advances and the Future Generation of Neuroinformatics Infrastructure, 2015. 22. J. Ma, H. T. Ma, H. Li, C. Ye, D. Wu, X. Tang, M. Miller, and S. Mori, “A fast atlas pre-selection procedure for multi- atlas based brain segmentation," in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 3053-3056, IEEE, 2015. 23. M. S. Albert, A. Soldan, O. Selnes, M. Miller, T. Ratnanather, S. Mori, A. Moghekar, R. O'Brien, R. Scherer, S. Li, et al., “Using combinations of variables to identify individuals with preclinical ad," Alzheimer's & Dementia, vol. 11, no. 7, p. P118, 2015. 24. Z. Liang, X. He, C. Ceritoglu, X. Tang, Y. Li, K. S. Kutten, K. Oishi, M. I. Miller, S. Mori, and A. V. Faria, “Evaluation of cross-protocol stability of a fully automated brain multi-atlas parcellation tool," PloS one, vol. 10, no. 7, p. e0133533, 2015. 25. A. Soldan, C. Pettigrew, Y. Lu, M.-C. Wang, O. Selnes, M. Albert, T. Brown, J. T. Ratnanather, L. Younes, and M. I. Miller, “Relationship of medial temporal lobe atrophy, apoe genotype, and cognitive reserve in preclinical alzheimer's disease," Human brain mapping, vol. 36, no. 7, pp. 2826-2841, 2015. 26. X. Tang, D. Holland, A. M. Dale, L. Younes, and M. I. Miller, “The dieomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and alzheimer's disease," Human brain mapping, vol. 36, no. 6, pp. 2093-2117, 2015. 27. C. L. Davis, K. Oishi, A. V. Faria, J. Hsu, Y. Gomez, S. Mori, and A. E. Hillis, “White matter tracts critical for recognition of sarcasm," Neurocase, vol. 22, no. 1, pp. 22-29, 2016. 28. L. A. Jacobson, D. J. Peterson, K. S. Rosch, D. Crocetti, S. Mori, and S. H. Mostofsky, “Sex-based dissociation of white matter microstructure in children with attention-deficit/hyperactivity disorder," Journal of the American Academy of Child & Adolescent Psychiatry, vol. 54, no. 11, pp. 938-946, 2015. 29. V. R. Varma, X. Tang, and M. C. Carlson, “Hippocampal sub-regional shape and physical activity in older adults," Hippocampus, 2016. 30. D. Tward, M. Miller, A. Trouve, and L. Younes, “Parametric surface diffeomorphometry for low dimensional embeddings of dense segmentations and imagery," 2016. 31. D. J. Tward, A. Bakker, M. Gallagher, and M. Miller, “Changes in medial temporal lobe anatomy quantified using probabilistic atlas construction and surface diffeomorphometry," Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol. 11, no. 7, pp. P49-P50, 2015. 32. D. Tward, A. Kolasny, N. Charon, M. Miller, and L. Younes, “GPU acceleration on the stampede cluster for the computational anatomy gateway," in Proceedings of the 2015 Annual Conference on Extreme Science and Engineering Discovery Environment, ACM, 2015.
PY6 IPR 1 Page 87 33. D. J. Tward, C. C. Sicat, T. Brown, E. A. Miller, J. T. Ratnanather, L. Younes, A. Bakker, M. Albert, M. Gallagher, M. S., and M. I. Miller, “Local atrophy of entorhinal and transentorhinal cortex in mild cognitive impairment measured via diffeomorphometry," in Neuroscience 2016, Society for Neuroscience, November 2016. accepted. 34. D. J. Tward, A. Kolasny, C. S. Sicat, T. Brown, and M. Miller, “Tools for studying populations and timeseries of neuroanatomy enabled through GPU acceleration in the computational anatomy gateway," in Proceedings of the 2016 Annual Conference on Extreme Science and Engineering Discovery Environment, ACM, 2016. accepted. 6. TG-AST020001S 35. Marcus, P. S., S. Pei, C.-H. Jiang, J. A. Barranco, P. Hassanzadeh, and D. Lecoanet (2015), ZOMBIE VORTEX INSTABILITY. I. A PURELY HYDRODYNAMIC INSTABILITY TO RESURRECT THE DEAD ZONES OF PROTOPLANETARY DISKS, The Astrophysical Journal, 808(1), 87, doi:10.1088/0004-637x/808/1/87. (published) [Stampede, TACC, Comet] 36. Mahdinia, M., Hassanzadeh, P., Marcus, P., Jiang, C. 2016. Stability of 3D Gaussian vortices in rotating stratified Boussinesq flows: Linear analysis. arXiv preprint arXiv:1605.06859, accepted, revision required, Journal of Fluid Mechanics. (published) [Comet] 37. Marcus, P., Pei, S., Jiang, C., Barranco, J. 2016. Zombie Vortex Instability. II. Thresholds to Trigger Instability and the Properties of Zombie Turbulence in the Dead Zones of Protoplanetary Disks. arXiv preprint arXiv:1605.07635, under review by the Astrophysical Journal. (published) [Comet] 38. Marcus, P., Tollefson, J., de Pater, I. 2015. A New Generalized Thermal Wind Equation and its Application to Zonal Flows on the Gas Giant Planets. APS Meeting Abstracts. (published) [Comet] 39. Sromovsky, L., de Pater, I., Fry, P., Hammel, H., Marcus, P. 2015. High S/N Keck and Gemini AO imaging of Uranus during 2012--2014: New cloud patterns, increasing activity, and improved wind measurements. Icarus 258: 192--223. (published) [Comet] 40. Tollefson, J., de Pater, I., Marcus, P., Luszcz-Cook, S. 2016. Neptune’s Vertical Wind Shear Can Only be Modeled with a Generalized Thermal Wind Equation . Icarus. (published) [Comet] 41. Wang, M., Huerre, P., Jiang, C., Pei, S., Rui, M., et al. 2015. Baroclinic Critical Layers and the Zombie Vortex Instability (ZVI) in Stratified, Rotating Shear Flows: Where They Form and Why. APS Meeting Abstracts. (published) [Comet] 7. TG-AST060019N, TG-AST060020N, TG-AST080028N, TG-AST090005 42. Lee, K.-Y., G. Mellema, and P. Lundqvist (2015), Efficient photoheating algorithms in time-dependent photoionization simulations, Monthly Notices of the Royal Astronomical Society, 455(4), 4406–4425, doi:10.1093/mnras/stv2556. (published) [TACC] 8. TG-AST060020N, TG-AST090005 43. Park (박현배), H., E. Komatsu, P. R. Shapiro, J. Koda, and Y. Mao (2016), THE IMPACT OF NONLINEAR STRUCTURE FORMATION ON THE POWER SPECTRUM OF TRANSVERSE MOMENTUM FLUCTUATIONS AND THE KINETIC SUNYAEV–ZEL’DOVICH EFFECT, The Astrophysical Journal, 818(1), 37, doi:10.3847/0004- 637x/818/1/37. (published) [Lonestar, Ranch, Stampede, TACC]
9. TG-AST080026N 44. Bilous, A. V. et al. (2016), A LOFAR census of non-recycled pulsars: average profiles, dispersion measures, flux densities, and spectra, A&A, 591, A134, doi:10.1051/0004-6361/201527702. (published) 45. Bilous, A., Kondratiev, V., Kramer, M., Keane, E., Hessels, J., et al. 2016. VizieR Online Data Catalog: LOFAR census of non-recycled pulsars (Bilous+, 2016). VizieR Online Data Catalog 359. (published) 46. Broderick, J. W. et al. (2016), Low-radio-frequency eclipses of the redback pulsar J2215+5135 observed in the image plane with LOFAR, Monthly Notices of the Royal Astronomical Society, 459(3), 2681–2689, doi:10.1093/mnras/stw794. (published) 47. Carbone, D. et al. (2016), New methods to constrain the radio transient rate: results from a survey of four fields with LOFAR, Monthly Notices of the Royal Astronomical Society, 459(3), 3161–3174, doi:10.1093/mnras/stw539. (published)
PY6 IPR 1 Page 88 48. Crosley, M., Osten, R., Broderick, J., Corbel, S., Eisloffel, J., et al. 2016. The Search for Signatures Of Transient Mass Loss in Active Stars. ArXiv e-prints. (published) 49. Fish, V., Akiyama, K., Bouman, K., Chael, A., Johnson, M., et al. 2016. Observing—and Imaging—Active Galactic Nuclei with the Event Horizon Telescope. ArXiv e-prints. (published) 50. Gammie, C., Liao, W., Ricker, P. 2016. A hot big bang theory: magnetic fields and the early evolution of the protolunar disk. ArXiv e-prints. (published) 51. O’ Riordan, M., Pe’er, A., McKinney, J. 2016. Effects of Spin on High-Energy Radiation from Accreting Black Holes. ArXiv e-prints. (published) 52. Ortiz-León, G. N. et al. (2016), THE INTRINSIC SHAPE OF SAGITTARIUS A* AT 3.5 mm WAVELENGTH, The Astrophysical Journal, 824(1), 40, doi:10.3847/0004-637x/824/1/40. (published) 10. TG-AST080026N, TG-AST080028 53. Narayan, R., Y. Zhu, D. Psaltis, and A. Sa̧dowski (2016), heroic: 3D general relativistic radiative post-processor with comptonization for black hole accretion discs, Monthly Notices of the Royal Astronomical Society, 457(1), 608–628, doi:10.1093/mnras/stv2979. (published) [Stampede] 54. Sądowski, A. (2016), Thin accretion discs are stabilized by a strong magnetic field, Monthly Notices of the Royal Astronomical Society, 459(4), 4397–4407, doi:10.1093/mnras/stw913. (published) [Stampede] 55. Sądowski, A., J.-P. Lasota, M. A. Abramowicz, and R. Narayan (2016), Energy flows in thick accretion discs and their consequences for black hole feedback, Monthly Notices of the Royal Astronomical Society, 456(4), 3915– 3928, doi:10.1093/mnras/stv2854. (published) [Stampede] 56. Sądowski, A., and R. Narayan (2015), Photon-conserving Comptonization in simulations of accretion discs around black holes, Monthly Notices of the Royal Astronomical Society, 454(3), 2372–2380, doi:10.1093/mnras/stv2022. (published) [Stampede] 57. Sądowski, A., and R. Narayan (2015), Powerful radiative jets in supercritical accretion discs around non- spinning black holes, Monthly Notices of the Royal Astronomical Society, 453(3), 3214–3222, doi:10.1093/mnras/stv1802. (published) [Stampede] 58. Sądowski, A., and R. Narayan (2016), Three-dimensional simulations of supercritical black hole accretion discs – luminosities, photon trapping and variability, Monthly Notices of the Royal Astronomical Society, 456(4), 3929–3947, doi:10.1093/mnras/stv2941. (published) [Stampede] 59. Sądowski, A., E. Tejeda, E. Gafton, S. Rosswog, and D. Abarca (2016), Magnetohydrodynamical simulations of a deep tidal disruption in general relativity, Monthly Notices of the Royal Astronomical Society, 458(4), 4250– 4268, doi:10.1093/mnras/stw589. (published) [Stampede] 60. Sadowski, A., Wielgus, M., Narayan, R., Abarca, D., McKinney, J. 2016. Radiative, two-temperature simulations of low luminosity black hole accretion flows in general relativity. Monthly Notices of the Royal Astronomical Society. (submitted) [Stampede] 11. TG-AST080028N, TG-AST090005 61. Bisbas, T. G. et al. (2015), starbench: the D-type expansion of an H ii region, Monthly Notices of the Royal Astronomical Society, 453(2), 1324–1343, doi:10.1093/mnras/stv1659. (published) [Lonestar, Ranch, TACC] 12. TG-AST090005 62. Ahn, K. (2015), LIGHT-CONE EFFECT OF RADIATION FIELDS IN COSMOLOGICAL RADIATIVE TRANSFER SIMULATIONS, Journal of The Korean Astronomical Society, 48(1), 67–73, doi:10.5303/jkas.2015.48.1.67. (published) [Lonestar, Ranch, Stampede, TACC] 63. Ahn, K. 2016. How Density Environment Changes the Influence of the Dark Matter-Baryon Streaming Velocity on the Cosmological Structure Formation. ArXiv e-prints. (published) [Lonestar, Ranch, Stampede, TACC] 64. Ahn, K., Mesinger, A., Alvarez, M., Chen, X. 2015. Probing the First Galaxies and Their Impact on the Intergalactic Medium through 21-cm Observations of the Cosmic Dawn with the SKA. Advancing Astrophysics with the Square Kilometre Array (AASKA14): 3. (published) [Lonestar, Ranch, TACC] 65. Camera, S., Raccanelli, A., Bull, P., Bertacca, D., Chen, X., et al. 2015. Cosmology on the Largest Scales with the SKA. Advancing Astrophysics with the Square Kilometre Array (AASKA14): 25. (published) [Lonestar, Ranch, TACC] 66. Iliev, I., Santos, M., Mesinger, A., Majumdar, S., Mellema, G. 2015. “Epoch of Reionization modelling and simulations for SKA.” Advancing Astrophysics with the Square Kilometre Array (AASKA14): 7. (published) [Lonestar, Ranch, Stampede, TACC]
PY6 IPR 1 Page 89 67. Jensen, H., S. Majumdar, G. Mellema, A. Lidz, I. T. Iliev, and K. L. Dixon (2015), The wedge bias in reionization 21-cm power spectrum measurements, Monthly Notices of the Royal Astronomical Society, 456(1), 66–70, doi:10.1093/mnras/stv2679. (published) [Lonestar, Ranch, Stampede, TACC] 68. Koopmans, L., Pritchard, J., Mellema, G., Aguirre, J., Ahn, K., et al. 2015. The Cosmic Dawn and Epoch of Reionisation with SKA. Advancing Astrophysics with the Square Kilometre Array (AASKA14): 1. (published) [Lonestar, Ranch, Stampede, TACC] 69. Majumdar, S. et al. (2015), Effects of the sources of reionization on 21-cm redshift-space distortions, Monthly Notices of the Royal Astronomical Society, 456(2), 2080–2094, doi:10.1093/mnras/stv2812. (published) [Lonestar, Ranch, TACC] 70. Majumdar, S., G. Mellema, K. K. Datta, H. Jensen, T. R. Choudhury, S. Bharadwaj, and M. M. Friedrich (2014), On the use of seminumerical simulations in predicting the 21-cm signal from the epoch of reionization, Monthly Notices of the Royal Astronomical Society, 443(4), 2843–2861, doi:10.1093/mnras/stu1342. (published) [Lonestar, Ranch, Stampede, TACC] 71. Mao, Y., DAloisio, A., Wandelt, B., Zhang, J., Shapiro, P. 2015. Linear perturbation theory of reionization in position space: Cosmological radiative transfer along the light cone. Physical Review D. 91, 083015 (published) [Ranch, TACC] 72. Mesinger, A., Ferrara, A., Greig, B., Iliev, I., Mellema, G., et al. 2015. Constraining the Astrophysics of the Cosmic Dawn and the Epoch of Reionization with the SKA. Advancing Astrophysics with the Square Kilometre Array (AASKA14): 11. (published) [Lonestar, Ranch, Stampede, TACC] 73. Ocvirk, P., Gillet, N., Shapiro, P., Aubert, D., Iliev, I., et al. 2015. Cosmic Dawn (CoDa): the First Radiation- and Galaxy Formation in the Local Universe. MNRAS. (accepted) [Lonestar, Ranch, Stampede, TACC] 74. Park, H., Shapiro, P., Choi, J., Yoshida, N., Hirano, S., et al. 2016. The Hydrodynamic Feedback of Cosmic Reionization on Small-Scale Structures and Its Impact on Photon Consumption during the Epoch of Reionization. The Astrophysical Journal. (accepted) [Lonestar, Ranch, Stampede, TACC] 75. Semelin, B., Iliev, I. 2015. The physics of Reionization: processes relevant for SKA observations. Advancing Astrophysics with the Square Kilometre Array (AASKA14): 13. (published) [Lonestar, Ranch, TACC] 76. Shukla, H., G. Mellema, I. T. Iliev, and P. R. Shapiro (2016), The effects of Lyman-limit systems on the evolution and observability of the epoch of reionization, Monthly Notices of the Royal Astronomical Society, 458(1), 135–150, doi:10.1093/mnras/stw249. (published) [Lonestar, Ranch, Stampede, TACC] 77. Vrbanec, D. et al. (2016), Predictions for the 21 cm-galaxy cross-power spectrum observable with LOFAR and Subaru, Monthly Notices of the Royal Astronomical Society, 457(1), 666–675, doi:10.1093/mnras/stv2993. (published) [Lonestar, Ranch, Stampede, TACC] 78. Xu, H., Ahn, K., Norman, M., Wise, J., O’Shea, B. 2016. X-ray Background at High Redshifts from Pop III Remnants: Results from Pop III star formation rates in the Renaissance Simulations. ArXiv e-prints. (published) [Lonestar, Ranch, TACC] 79. Xu, H., Wise, J., Norman, M., Ahn, K., O’Shea, B. 2016. Galaxy Properties and UV Escape Fractions During Epoch of Reionization: Results from the Renaissance Simulations. ArXiv e-prints. (published) [Lonestar, Ranch, TACC] 80. Rindler-Daller, T., & Shapiro, P. R. 2014, “Complex Scalar Field Dark Matter on Galactic Scales," Modern Physics Letters A, 29, 30002 (http://adsabs.harvard.edu/abs/2014MPLA...2930002R) 81. Li, B., Rindler-Daller, T., & Shapiro, P. 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PY6 IPR 1 Page 94 27. TG-AST140041 153. Petri, A. (2016), Mocking the weak lensing universe: The LensTools Python computing package, Astronomy and Computing, 17, 73–79, doi:10.1016/j.ascom.2016.06.001. (published) [Ranch, Stampede, TACC] 28. TG-AST140064 154. Wetzel, A., Hopkins, P., Kim, J., Faucher-Giguere, C., Keres, D., et al. 2016. Reconciling dwarf galaxies with LCDM cosmology: Simulating a realistic population of satellites around a Milky Way-mass galaxy. Astrophysical Journal Letters. (submitted) 155. “Metal flows of the circumgalactic medium, and the metal budget in galactic halos” A. L. Muratov, D. Keres, C. A. Faucher-Giguere, P. F. Hopkins, X. Ma, D. Angles-Alcazar, T. K. Chan, P. Torrey, Z. Hafen, E. Quataert, N. Murray, Monthly Notices of the Royal Astronomical Society, 2016, in press, [arXiv:1606.09252] 156. “The impact of stellar feedback on hot gas in galaxy haloes: the Sunyaev-Zel’dovich effect and soft X-ray emission” F. van de Voort, E. Quataert, P. F. Hopkins, C. A. Faucher-Giguere, R. Feldmann, D. Keres, T. K. Chan, Z. H. Hafen, 2016, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1604:01397] 157. “Gravitational torque-driven black hole growth and feedback in cosmological simulations” D. Angles-Alcazar, R. Dave, C. A. Faucher-Giguere, F. Ozel, P. F. Hopkins, 2016, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1603.08007] 158. “Giant clumps in the FIRE simulations: a case study of a massive high-redshift galaxy” A. Oklopcic, P. F. Hopkins, R. Feldmann, D. Keres, C. A. Faucher-Giguere, N. Murray, 2016, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1603.03778] 159. “Anisotropic Diffusion in Mesh-Free Numerical Magnetohydrodynamics” P. F. Hopkins, 2016, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1602.07703] 160. “Reconciling dwarf galaxies with LCDM cosmology: Simulating a realistic population of satellites around a Milky Way-mass galaxy” A. R. Wetzel, P. F. Hopkins, J. H. Kim, C. A. Faucher-Giguere, D. Keres, E. Quataert, 2016, The Astrophysical Journal, in press [arXiv:1602.05957] 161. “Binary Stars Can Provide the Missing Photons Needed for Reionization” X. Ma, P. F. Hopkins, D. Kasen, E. Quataert, C. A. Faucher-Giguere, D. Keres, N. Murray, Monthly Notices of the Royal Astronomical Society, 2016, 459, 3614 162. “MUFASA: Galaxy Formation Simulations with Meshless Hydrodynamics” R. Dav´e, R. J. Thompson, P. F. Hopkins, 2016, Monthly Notices of the Royal Astronomical Society, in press, [arXiv:1604.01418] 163. “The formation of massive, quiescent galaxies at cosmic noon” R. Feldmann, P. F. Hopkins, E. Quataert, C. A. Faucher-Giguere, D. Keres, 2016, Monthly Notices of the Royal Astronomical Society, 458, 14 164. “An instability of feedback regulated star formation in galactic nuclei” P. Torrey, P. F. Hopkins, C. A. Faucher- Giguere, M. Vogelsberger, E. Quataert, D. Keres, N. Murray, 2016, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1601.07186] 165. “A Stellar Feedback Origin for Neutral Hydrogen in High-Redshift Quasar-Mass Halos” C. A. Faucher-Giguere, R. Feldmann, E. Quataert, D. Keres, P. F. Hopkins, N. Murray, 2016, Monthly Notices of the Royal Astronomical Society, 461, 32 166. “Breathing FIRE: How Stellar Feedback Drives Radial Migration, Rapid Size Fluctuations, and Population Gradients in Low-Mass Galaxies” K. El-Badry, A. R. Wetzel, M. Geha, P. F. Hopkins, D. Keres, T. K. Chan, C. A. Faucher-Giguere, 2016, The Astrophysical Journal, 820, 131 167. “The no-spin zone: rotation vs dispersion support in observed and simulated dwarf galaxies” C. Wheeler, A. B. Pace, J. S. Bullock, M. Boylan-Kolchin, J. Onorbe, A. Fitts, P. F. Hopkins, D. Keres, 2015, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1511.01095] 168. “Strongly Time-Variable Ultra-Violet Metal Line Emission from the Circum-Galactic Medium of High-Redshift Galaxies” N. Sravan, C. A. Faucher-Giguere, F. van de Voort, D. Keres, A. L. Muratov, P. F. Hopkins, R. Feldmann, E. Quataert, N. Murray, 2015, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1510.06410] 169. “The Necessity of Feedback Physics in Setting the Peak of the Initial Mass Function” D. Guszejnov, M. R. Krumholz, P. F. Hopkins, 2016, Monthly Notices of the Royal Astronomical Society, 458, 673 170. “(Star)bursts of FIRE: observational signatures of bursty star formation in galaxies” M. Sparre, C. C. Hayward, R. Feldmann, C. A. Faucher-Giguere, A. L. Muratov, D. Keres, P. F. Hopkins, 2015, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1510.03869] 171. “A Constrained-Gradient Method to Control Divergence Errors in Numerical MHD” P. F. Hopkins, 2015, Monthly Notices of the Royal Astronomical Society, in press [arXiv:1509.07877] 172. “The fundamentally different dynamics of dust and gas in molecular clouds” P. F. Hopkins, H. Lee, 2016, Monthly Notices of the Royal Astronomical Society, 456, 4174
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PY6 IPR 1 Page 104 339. Hong, X., D. A. Bercovici, Z. Yang, N. Al-Bataineh, R. Srinivasan, R. C. Dhakal, K. N. Houk, and M. Brewer (2015), Mechanism and Dynamics of Intramolecular C–H Insertion Reactions of 1-Aza-2-azoniaallene Salts, Journal of the American Chemical Society, 137(28), 9100–9107, doi:10.1021/jacs.5b04474. (published) 340. Hong, X., J. Wang, Y.-F. Yang, L. He, C.-Y. Ho, and K. N. Houk (2015), Computational Exploration of Mechanism and Selectivities of (NHC)Nickel(II)hydride-Catalyzed Hydroalkenylations of Styrene with α-Olefins, ACS Catalysis, 5(9), 5545–5555, doi:10.1021/acscatal.5b01075. (published) 341. Hooper, J. F., N. C. James, E. Bozkurt, V. Aviyente, J. M. White, M. C. Holland, R. Gilmour, A. B. Holmes, and K. N. Houk (2015), Medium-Ring Effects on the Endo/Exo Selectivity of the Organocatalytic Intramolecular Diels– Alder Reaction , J. Org. Chem., 80(24), 12058–12075, doi:10.1021/acs.joc.5b02037. (published) 342. Jiang, X., Z. J. O’Brien, S. Yang, L. H. Lai, J. Buenaflor, C. Tan, S. Khan, K. N. Houk, and M. A. Garcia-Garibay (2016), Crystal Fluidity Reflected by Fast Rotational Motion at the Core, Branches, and Peripheral Aromatic Groups of a Dendrimeric Molecular Rotor, Journal of the American Chemical Society, 138(13), 4650–4656, doi:10.1021/jacs.6b01398. (published) 343. Jung, M. E., C. A. Roberts, F. Perez, H. V. Pham, L. Zou, and K. N. Houk (2016), Thermodynamic Control of Isomerizations of Bicyclic Radicals: Interplay of Ring Strain and Radical Stabilization, Organic Letters, 18(1), 32–35, doi:10.1021/acs.orglett.5b03112. (published) 344. Kamber, D. N., Y. Liang, R. J. Blizzard, F. Liu, R. A. Mehl, K. N. Houk, and J. A. Prescher (2015), 1,2,4-Triazines Are Versatile Bioorthogonal Reagents, Journal of the American Chemical Society, 137(26), 8388–8391, doi:10.1021/jacs.5b05100. (published) 345. Krenske, E. H., S. Lam, J. P. L. Ng, B. Lo, S. K. Lam, P. Chiu, and K. N. 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Tang (2016), P450-Mediated Coupling of Indole Fragments To Forge Communesin and Unnatural Isomers, Journal of the American Chemical Society, 138(12), 4002–4005, doi:10.1021/jacs.6b01413. (published) 350. Mao, X.-M. et al. (2015), Efficient Biosynthesis of Fungal Polyketides Containing the Dioxabicyclo-octane Ring System, Journal of the American Chemical Society, 137(37), 11904–11907, doi:10.1021/jacs.5b07816. (published) 351. Martinez-Solorio, D., B. Melillo, L. Sanchez, Y. Liang, E. Lam, K. N. Houk, and A. B. Smith (2016), Design, Synthesis, and Validation of an Effective, Reusable Silicon-Based Transfer Agent for Room-Temperature Pd- Catalyzed Cross-Coupling Reactions of Aryl and Heteroaryl Chlorides with Readily Available Aryl Lithium Reagents, Journal of the American Chemical Society, 138(6), 1836–1839, doi:10.1021/jacs.5b13260. (published) 352. Narayanam, M. K., Y. Liang, K. N. Houk, and J. M. 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Houk (2015), Computational and Experimental Investigations of the Formal Dyotropic Rearrangements of Himbert Arene/Allene Cycloadducts, Journal of the American Chemical Society, 137(21), 6956–6964, doi:10.1021/jacs.5b03718. (published) 356. Rodríguez, E., M. N. Grayson, A. Asensio, P. Barrio, K. N. Houk, and S. Fustero (2016), Chiral Brønsted Acid- Catalyzed Asymmetric Allyl(propargyl)boration Reaction of ortho -Alkynyl Benzaldehydes: Synthetic Applications and Factors Governing the Enantioselectivity , ACS Catalysis, 6(4), 2506–2514, doi:10.1021/acscatal.6b00209. (published)
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PY6 IPR 1 Page 106 376. Sliwinska-Bartkowiak, M., M. Jazdzewska, M. Trafas, M. Kaczmarek-Klinowska, and K. E. Gubbins (2015), Melting of Eutectic Mixtures in Silica and Carbon Nanopores, J. Chem. Eng. Data, 60(11), 3093–3100, doi:10.1021/acs.jced.5b00131. (published) 54. TG-CHE100118 377. Driscoll, D., McEntee, M., Tang, W., Neurock, M., Morris, J. 2016. Infrared and theoretical study of propene adsorption on metal sites of Au/TiO2. Journal of the American Chemical Society. (submitted) [Stampede] 378. Neurock, M., Tang, W. 2016. Engineering active sites and their environments under working catalytic conditions. 251st American Chemical Society National Meeting (San Diego). (published) [Stampede, TACC] 379. Neurock, M., Tang, W. 2016. Selective oxidation at the Au/TiO2 interface. 251st American Chemical Society National Meeting (San Diego). (published) [Stampede, TACC] 380. Panayotov, D., M. McEntee, S. Burrows, D. Driscoll, W. Tang, M. Neurock, and J. Morris (2016), Infrared studies of propene and propene oxide adsorption on nanoparticulate Au/TiO2, Surface Science, 652, 172–182, doi:10.1016/j.susc.2016.03.033. (published) [Stampede, TACC] 381. Wang, J., M. McEntee, W. Tang, M. Neurock, A. P. Baddorf, P. Maksymovych, and J. T. Yates (2016), Formation, Migration, and Reactivity of Au–CO Complexes on Gold Surfaces, Journal of the American Chemical Society, 138(5), 1518–1526, doi:10.1021/jacs.5b09052. (published) [Stampede, TACC] 55. TG-CHE100150 382. Fiedler, S. L., and J. Eloranta (2013), Interaction of Helium Rydberg State Atoms with Superfluid Helium, Journal of Low Temperature Physics, 174(5-6), 269–283, doi:10.1007/s10909-013-0991-6. (published) 56. TG-CHE110065, TG-CHE130057, TG-CHE140089, TG-MCB130155, TG-MCB140159 383. Xiao, X., Agris, P., Hall, C. 2016. Introducing folding stability into the score function for computational design of RNA-binding peptides boosts the probability of success. Proteins 84: 700-711. (published) [NICS, PSC, SDSC, TACC] 57. TG-CHE110084 384. Morrell, T. E., I. U. Rafalska-Metcalf, J.-W. Chu, and H. Yang (2014), Coupling between Protein Conformation and Local Unfolding Highlights the Role of Disorder in Protein Function and Suggests a New Target for Tuberculosis Treatment, Biophysical Journal, 106(2), 257a, doi:10.1016/j.bpj.2013.11.1509. (published) 58. TG-CHE110085 385. Chagarov, E. A., L. Porter, and A. C. Kummel (2016), Density-functional theory molecular dynamics simulations of a-HfO2/Ge(100)(2 × 1) and a-ZrO2/Ge(100)(2 × 1) interface passivation, J. Chem. Phys., 144(8), 084704, doi:10.1063/1.4941947. (published) [Comet, SDSC] 386. Chagarov, E., K. Sardashti, A. C. Kummel, Y. S. Lee, R. Haight, and T. S. Gershon (2016), Ag2ZnSn(S,Se)4: A highly promising absorber for thin film photovoltaics, J. Chem. Phys., 144(10), 104704, doi:10.1063/1.4943270. (published) [Comet, SDSC] 387. Edmonds, M., T. Kent, E. Chagarov, K. Sardashti, R. Droopad, M. Chang, J. Kachian, J. H. Park, and A. Kummel (2015), Passivation of InGaAs(001)-(2 × 4) by Self-Limiting Chemical Vapor Deposition of a Silicon Hydride Control Layer, Journal of the American Chemical Society, 137(26), 8526–8533, doi:10.1021/jacs.5b03660. (published) [Comet, SDSC] 388. Edmonds, M., Kent, T., Wolf, S., Sardashti, K., Chang, M., et al. 2016. In0.53Ga0.47As(001)-(2x4) and Si0.5Ge0.5(110) surface passivation by self-limiting deposition of silicon containing control layers. 2016 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA) (Hsinchu, Taiwan). 1-2. http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=7480528. (published) [Comet, SDSC] 389. Kent, T., E. Chagarov, M. Edmonds, R. Droopad, and A. C. Kummel (2015), Dual Passivation of Intrinsic Defects at the Compound Semiconductor/Oxide Interface Using an Oxidant and a Reductant, ACS Nano, 9(5), 4843– 4849, doi:10.1021/nn5063003. (published) [Comet, SDSC] 390. Park, S. W., T. Kaufman-Osborn, H. Kim, S. Siddiqui, B. Sahu, N. Yoshida, A. Brandt, and A. C. Kummel (2015), Combined wet and dry cleaning of SiGe(001), J. Vac. Sci. Technol. A, 33(4), 041403, doi:10.1116/1.4922282. (published) [Comet, SDSC] 391. Sardashti, K., R. Haight, T. Gokmen, W. Wang, L.-Y. Chang, D. B. Mitzi, and A. C. Kummel (2015), Impact of Nanoscale Elemental Distribution in High-Performance Kesterite Solar Cells, Advanced Energy Materials, 5(10), 1402180, doi:10.1002/aenm.201402180. (published) [Comet, SDSC]
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PY6 IPR 1 Page 108 409. Yan, L., J. E. Elenewski, W. Jiang, and H. Chen (2015), Computational modeling of self-trapped electrons in rutile TiO 2 , Phys. Chem. Chem. Phys., 17(44), 29949–29957, doi:10.1039/c5cp05271h. (published) [Stampede, TACC] 63. TG-CHE130010 410. Han Du, W.-G., A. W. Götz, L. Yang, R. C. Walker, and L. Noodleman (2016), A broken-symmetry density functional study of structures, energies, and protonation states along the catalytic O–O bond cleavage pathway in ba3cytochrome c oxidase from Thermus thermophilus, Phys. Chem. Chem. Phys., 18(31), 21162– 21171, doi:10.1039/c6cp00349d. (published) [Comet, Gordon, SDSC] 411. Yang, L., Å. A. Skjevik, W.-G. Han Du, L. Noodleman, R. C. Walker, and A. W. Götz (2016), Water exit pathways and proton pumping mechanism in B-type cytochrome c oxidase from molecular dynamics simulations, Biochimica et Biophysica Acta (BBA) - Bioenergetics, 1857(9), 1594–1606, doi:10.1016/j.bbabio.2016.06.005. (published) [Comet, SDSC] 412. Yang, L., Skjevik, Å., Han Du, W., Noodleman, L., Walker, R., et al. 2016. Data for molecular dynamics simulations of B-type cytochrome c oxidase with the Amber force field. Data in Brief 8: 1209-1214. http://dx.doi.org/10.1016/j.dib.2016.07.043 (published) [Comet, SDSC] 64. TG-CHE130035, TG-CTS120029 413. Avanesian, T., and P. Christopher (2016), Scaled Degree of Rate Control: Identifying Elementary Steps That Control Differences in Performance of Transition-Metal Catalysts, ACS Catalysis, 6(8), 5268–5272, doi:10.1021/acscatal.6b01547. (published) [Comet, SDSC, Trestles] 65. TG-CHE130094 414. Dore, E. M., and J. T. Lyon (2016), The Structures of Silicon Clusters Doped with Two Gold Atoms, Si n Au2 (n = 1–10), Journal of Cluster Science, 27(4), 1365–1381, doi:10.1007/s10876-016-1006-y. (published) [Comet, SDSC] 415. Lyon, J. T.; Cho, H.-G.; Andrews, L. “Matrix Infrared Spectroscopic and Theoretical Studies of the Reactions Between Group 5 Transition Metals and CX4 Molecules (X = H, F, and Cl)” Journal of Physical Chemistry A 2015, 119, 12742-12755. 416. Dore, E. M.; Lyon, J. T. “The Structures of Silicon Clusters Doped with Two Gold Atoms, SinAu2 (n = 1 - 10)” Journal of Cluster Science 2016, accepted. 417. Li, Y.; Lyon, J. T.; Woodham, A.; Lievens, P.; Fielicke, A.; Janssens, E. “The geometric structure of silver doped silicon clusters” ChemPhysChem 2014, 15, 328-336. 418. Li, Y.; Lyon, J. T.; Woodham, A.; Lievens, P.; Fielicke, A.; Janssens, E. “Structural Identification of Gold Doped Silicon Clusters via Far-Infrared Spectroscopy” Journal of Physical Chemistry C 2015, 119, 10896-10903. 66. TG-CHE130109 419. Buchwald, J. R., S. Kal, M. R. Civic, I. M. deJoode, A. S. Filatov, and P. H. Dinolfo (2016), Spin modulation and electrochemical behavior of a five-coordinate cobalt(III) salen complex, Journal of Coordination Chemistry, 69(11-13), 1695–1708, doi:10.1080/00958972.2016.1175001. (published) [Comet, SDSC] 67. TG-CHE140073 420. Gani, T., Ioannidis, E., Kulik, H. 2016. Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation. submitted. (submitted) [Comet, Maverick, SDSC, TACC] 421. Ioannidis, E. I., T. Z. H. Gani, and H. J. Kulik (2016), molSimplify: A toolkit for automating discovery in inorganic chemistry, Journal of Computational Chemistry, 37(22), 2106–2117, doi:10.1002/jcc.24437. (published) [Comet, Maverick, SDSC, Stampede, TACC] 422. Kulik, H., Seelam, N., Mar, B., Martinez, T. 2016. Adapting DFT+U for the Chemically-Motivated Correction of Minimal Basis Set Incompleteness. Journal of Physical Chemistry A. (submitted) [Maverick, TACC] 423. Zhao, Q., Ioannidis, E., Kulik, H. 2016. Global and local curvature in density functional theory. submitted. (submitted) [Stampede, TACC] 424. Zhao, Q., Ng, S., Kulik, H. 2016. Predicting the Stability of Fullerene Allotropes Throughout the Periodic Table. Journal of Physical Chemistry C. (submitted) [Comet, Maverick, SDSC, TACC]
PY6 IPR 1 Page 109 68. TG-CHE140115 425. Cheng, X., J. D. Herr, and R. P. Steele (2016), Accelerating Ab Initio Path Integral Simulations via Imaginary Multiple-Timestepping , Journal of Chemical Theory and Computation, 12(4), 1627–1638, doi:10.1021/acs.jctc.6b00021. (published) 426. Cheng, X., J. D. Herr, and R. P. Steele (2016), Accelerating Ab Initio Path Integral Simulations via Imaginary Multiple-Timestepping , Journal of Chemical Theory and Computation, 12(4), 1627–1638, doi:10.1021/acs.jctc.6b00021. (published) 427. Pereverzev, A. Y., X. Cheng, N. S. Nagornova, D. L. Reese, R. P. Steele, and O. V. Boyarkin (2016), Vibrational Signatures of Conformer-Specific Intramolecular Interactions in Protonated Tryptophan, The Journal of Physical Chemistry A, 120(28), 5598–5608, doi:10.1021/acs.jpca.6b05605. (published) 428. Shao, Y. et al. (2014), Advances in molecular quantum chemistry contained in the Q-Chem 4 program package, Molecular Physics, 113(2), 184–215, doi:10.1080/00268976.2014.952696. (published) 429. Steele, R. P. (2015), Multiple-Timestep ab Initio Molecular Dynamics Using an Atomic Basis Set Partitioning , The Journal of Physical Chemistry A, 119(50), 12119–12130, doi:10.1021/acs.jpca.5b05850. (published) 69. TG-CHE140116 430. Kekenes-Huskey, P., Scott, C., Atalay, S. 2016. Quantifying the Influence of the Crowded Cytoplasm on Small Molecule Diffusion. J Phys Chem B: acs.jpcb.6b03887. (published) 431. Kucharski, A., Scott, C., Kekenes-Huskey, P. 2016. Understanding Ion Binding Affinity and Selectivity in Parvalbumin Using Molecular Dynamics and Mean Sphere Approximation Theory. J Phys Chem B. (published) 432. Scott, C., Kekenes-Huskey, P. 2016. Molecular Basis of S100A1 Activation at Saturating and Subsaturating Calcium Concentrations.. Biophys J 110: 1052--1063. (published) 70. TG-CHE150026 433. Hamann, C., Zehr, J., Tantillo, D. 2016. Is the Predicted Mechanism of Cycloseychellene Synthesis as (Z,Z) as It Seems?. 36th Reaction Mechanisms Conference (St. Louis, MO, United States). (published) [Stampede, TACC] 434. Sonntag, M., Hamann, C. 2016. Engaging Undergraduate Students with Raman Spectroscopy. 36th Reaction Mechanisms Conference (St. Louis, MO, United States). (published) [Stampede, TACC] 71. TG-CHE150048 435. Liao, C., Z. Zhang, J. Kale, D. W. Andrews, J. Lin, and J. Li (2016), Conformational Heterogeneity of Bax Helix 9 Dimer for Apoptotic Pore Formation, Scientific Reports, 6, 29502, doi:10.1038/srep29502. (published) [Stampede, TACC] 436. Li, J., S. Schneebeli, M. Sharafi, Z. Weinert, I. Cohen, C. Liao, and M. Ivancic (2016), Controlled Self-Assembly inside C-Shaped Polyaromatic Strips, Synlett, 27(14), 2145–2149, doi:10.1055/s-0035-1561479. (published) [Stampede, TACC] 437. Liao, C.; Zhang, Z.; Kale, J.; Andrews, D. W.; Lin, J.; Li, J. (corresponding author) “Conformational Heterogeneity of Bax Helix 9 Dimer for Apoptotic Pore Formation” Scientific Reports, 2016, 6, 29502. 438. Sharafi, M.; Weinert, Z.; Liao, C.; Li, J. (corresponding author) ; Schneebeli, S. T. “Controlled Self-Assembly inside C-Shaped Polyaromatic Strips” SYNLETT 2016, in press. (DOI: 10.1055/s-0035-1561479) 439. Zhang, Z.; Subramaniam, S.; Kale, J.; Liao, C.; Huang, B.; Brahmbhatt, H.: Condon, S. G. F.; Lapolla, S. M.; Hays, F. A.; Ding, J.; He, F.; Zhang, X. C.; Li, J.; Senes, A.; Andrews, D. W.; Lin, J. "BH3-in-Groove Dimerization Initiates and Helix 9 Dimerization Expands Bax Pore Assembly in Membranes” EMBO J. 2016, 35, 208. 440. Liu, X.; Weinert, Z.; Sharafi, M.; Liao, C.; Li, J. (corresponding author); Schneebeli, S. T. “Regulating Molecular Recognition with C-Shaped Strips Attained by Chirality-Assisted Synthesis” Angew. Chem. Int. Ed. 2015, 54, 12772. 441. Liao, C.; Selvan, M. E.; Zhao, J.; Slimovitch, J. L.; Schneebeli, S. T.; Shelley, M.; Shelley, J.; Li, J. (corresponding author) “Melittin Aggregation in Aqueous Solutions: Insight from Molecular Dynamics Simulations” J. Phys. Chem. B, 2015, 119, 10390. 72. TG-CHE150049, TG-CHE160003 442. Hendon, C. H., A. Walsh, N. Akiyama, Y. Konno, T. Kajiwara, T. Ito, H. Kitagawa, and K. Sakai (2016), One- dimensional Magnus-type platinum double salts, Nature Communications, 7, 11950, doi:10.1038/ncomms11950. (published) [Comet, SDSC, Stampede, TACC]
PY6 IPR 1 Page 110 73. TG-CHE150060 443. Kundu, S., Stieber, S., Ferrier, M., Kozimor, S., Bertke, J., et al. 2016. Redox Non-Innocence of Nitrosobenzene at Nickel. Angewandte Chemie. http://onlinelibrary.wiley.com/doi/10.1002/anie.201605026/full (published) [Gordon, SDSC] 74. TG-CHE160003 444. Wuttig, A., C. Liu, Q. Peng, M. Yaguchi, C. H. Hendon, K. Motobayashi, S. Ye, M. Osawa, and Y. Surendranath (2016), Tracking a Common Surface-Bound Intermediate during CO 2 -to-Fuels Catalysis , ACS Central Science, 2(8), 522–528, doi:10.1021/acscentsci.6b00155. (published) [Stampede, TACC] 75. TG-CHE160009 445. Izzo, J. 2016. Kinetic Isotope Effects and Theoretical Models Help Probe Long-Standing Mechanistic Debate for Diphenylprolinol Silyl Ether Catalyzed Michael Reaction Between Aldehydes and Nitroolefins. A poster was presented detailing the work done, experimentally and compuationally, on the elucidation of the widely debated mechanism of Micheal addition of aldehydes to nitroolefins. Gordon Research Conference - Stereochemistry. (published) [Comet, SDSC] 76. TG-CHE160034, TG-MCA05S027 446. Ganguly, P., N. F. A. van der Vegt, and J.-E. Shea (2016), Hydrophobic Association in Mixed Urea–TMAO Solutions, The Journal of Physical Chemistry Letters, 7(15), 3052–3059, doi:10.1021/acs.jpclett.6b01344. (published) [Comet, Data Oasis, SDSC, Stampede, TACC] 77. TG-CIE150028 447. Asudeh, A., Zhang, G., Hassan, N., Li, C., Zaruba, G. 2015. Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons. Proceeding of the 24th ACM conference on Information and knowledge management (CIKM). (published) 448. Hassan, N., Adair, B., Hamilton, J., Li, C., Tremayne, M., et al. 2015. The Quest to Automate Fact-Checking. Proceedings of the 2015 Computation+Journalism Symposium. (published) 449. Hassan, N., Feng, H., Venkataraman, R., Das, G., Li, C., et al. 2014. Anything You Can Do, I Can Do Better: Finding Expert Teams by CrewScout. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM), demonstration description. 2030--2032. (published) 450. Hassan, N., Li, C., Tremayne, M. 2015. Detecting Check-Worthy Factual Claims in Presidential Debates. Proceeding of the 24th ACM conference on Information and knowledge management (CIKM). (published) 451. Hassan, N., Tremayne, M., Arslan, F., Li, C. 2016. Comparing Automated Factual Claim Detection Against Judgments of Journalism Organizations. Proceedings of the 2016 Computation+Journalism Symposium. (published) 452. Jayaram, N., Bhoopalam, R., Li, C., Athitsos, V. 2016. Orion: Enabling Suggestions in a Visual Query Builder for Ultra-Heterogeneous Graphs. CoRR abs/1605.06856. (published) 453. Jayaram, N., Goyal, S., Li, C. 2015. VIIQ: Auto-Suggestion Enabled Visual Interface for Interactive Graph Query Formulation. Proceedings of the VLDB Endowment (PVLDB), demonstration description 8: 1940--1951. (published) 454. Jayaram, N., Gupta, M., Khan, A., Li, C., Yan, X., et al. 2014. GQBE: Querying knowledge graphs by example entity tuples. Proceedings of the 30th International Conference on Data Engineering (ICDE), demonstration description. 1250--1253. (published) 455. Jayaram, N., Khan, A., Li, C., Yan, X., Elmasri, R. 2014. Towards a Query-by-Example System for Knowledge Graphs. Proceedings of Workshop on GRAph Data Management Experiences and Systems (GRADES). 11:1-- 11:6. (published) 456. Jayaram, N., Khan, A., Li, C., Yan, X., Elmasri, R. 2016. Querying Knowledge Graphs by Example Entity Tuples. Proceedings of the 31st International Conference on Data Engineering (ICDE), TKDE poster track. 1494-- 1495. (published) 457. Li, C., He, B., Yan, N., Muhammad Assad Safiullah, . 2014. Set Predicates in SQL: Enabling Set-Level Comparisons for Dynamically Formed Groups. IEEE Transactions on Knowledge and Data Engineering (TKDE) 26: 438--452. (published) 458. Sultana, A., Hassan, N., Li, C., Yang, J., Yu, C. 2014. Incremental Discovery of Prominent Situational Facts. Proceedings of the 30th International Conference on Data Engineering(ICDE). 112--123. (published)
PY6 IPR 1 Page 111 459. Wu, Y., Agarwal, P., Li, C., Yang, J., Yu, C. 2014. Toward Computational Fact-Checking. Proceedings of the VLDB Endowment (PVLDB). 7. 589--600. (published) 460. Yan, N., Hasani, S., Asudeh, A., Li, C. 2016. Generating Preview Tables for Entity Graphs.. Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD). 1797--1811. (published) 461. Zhang, G., Jiang, X., Luo, P., Wang, M., Li, C. 2014. Discovering General Prominent Streaks in Sequence Data. ACM Transactions on Knowledge Discovery from Data (TKDD) 8: 9:1--9:37. (published) 462. Zhang, N., Li, C., Hassan, N., Rajasekaran, S., Das, G. 2014. On Skyline Groups. IEEE Transactions on Knowledge and Data Engineering (TKDE) 26: 942--956. (published) 78. TG-CTS070067N 463. Rastegari, A., Akhavan, R. 2015. Effect of Interface Curvature on Super-Hydrophobic Drag Reduction. 68th Annual Meeting of the APS Division of Fluid Dynamics (Boston, MA). 60/21. 596. (published) 464. Rastegari, A., Akhavan, R. 2016. The Common Mechanism of Turbulent Skin-Friction Drag Reduction with Super-Hydrophobic Longitudinal Micro-Grooves and Riblets. J. Fluid Mech.. (in preparation) 79. TG-CTS070070 465. Rastegari, A. & Akhavan, R. (2016) Structure and Dynamics of Turbulence in Super-Hydrophobic Channel Flows, In Progress in Wall Turbulence: Understanding and Modeling, M. Stanislas et al. (eds.), ERCOFTAC Series 23, 367-377, Springer. 466. Tang, Y. & Akhavan, R. (2016) Computation of high Reynolds number equilibrium and non-equilibrium wall- bounded turbulent flows using a nested-LES approach, In Progress in Wall Turbulence: Understanding and Modeling, M. Stanislas et al. (eds.), ERCOFTAC Series 23, 125-135, Springer. 467. A. Rastegari & R. Akhavan (2015) On the mechanism of turbulent drag reduction with super-hydrophobic surfaces, J. Fluid Mech. 773, R4. 468. Y. Tang & R. Akhavan (2016) Computations of equilibrium and non-equilibrium turbulent channel flows using a nested-LES approach, J. Fluid Mech. 973, 709-748. 469. A. Rastegari & R. Akhavan (2016) The common mechanism of turbulent skin-friction drag reduction with super-hydrophobic longitudinal micro-grooves and riblets, J. Fluid Mech. (in preparation). 80. TG-CTS080033, TG-CTS080043N 470. Khajeh-Saeed, A., and J. Blair Perot (2013), Direct numerical simulation of turbulence using GPU accelerated supercomputers, Journal of Computational Physics, 235, 241–257, doi:10.1016/j.jcp.2012.10.050. (published) [Keenland] 81. TG-CTS090025 471. Minh D Vo, ., Dimitrios V Papavassiliou, . 2016. The effects of shear and particle shape on the physical adsorption of polyvinyl pyrrolidone on carbon nanoparticles. Nanotechnology 27: 325709. http://stacks.iop.org/0957-4484/27/i=32/a=325709 (published) [Stampede, TACC] 82. TG-CTS100027 472. Limas, N. G., and T. A. Manz (2016), Introducing DDEC6 atomic population analysis: part 2. Computed results for a wide range of periodic and nonperiodic materials, RSC Adv., 6(51), 45727–45747, doi:10.1039/c6ra05507a. (published) [Comet, SDSC, Stampede, TACC, Trestles] 473. Manz, T. A., and N. G. Limas (2016), Introducing DDEC6 atomic population analysis: part 1. Charge partitioning theory and methodology, RSC Adv., 6(53), 47771–47801, doi:10.1039/c6ra04656h. (published) [Comet, SDSC, Stampede, TACC, Trestles] 474. Yang, B., and T. A. Manz (2016), Computationally designed tandem direct selective oxidation using molecular oxygen as oxidant without coreductant, RSC Adv., 6(91), 88189–88215, doi:10.1039/c6ra17731j. (published) [Comet, SDSC, Stampede, TACC] 83. TG-CTS100078 475. Mu, X., Z. Song, Y. Wang, Z. Xu, D. B. Go, and T. Luo (2016), Thermal transport in oxidized polycrystalline graphene, Carbon, 108, 318–326, doi:10.1016/j.carbon.2016.07.023. (published) [Comet, SDSC, Stampede, TACC, Trestles]
PY6 IPR 1 Page 112 476. Wu, X., V. Varshney, J. Lee, T. Zhang, J. L. Wohlwend, A. K. Roy, and T. Luo (2016), Hydrogenation of Penta- Graphene Leads to Unexpected Large Improvement in Thermal Conductivity, Nano Lett., 16(6), 3925–3935, doi:10.1021/acs.nanolett.6b01536. (published) [Comet, SDSC, Stampede, TACC, Trestles] 477. Zhang, T., and T. Luo (2016), Role of Chain Morphology and Stiffness in Thermal Conductivity of Amorphous Polymers, J. Phys. Chem. B, 120(4), 803–812, doi:10.1021/acs.jpcb.5b09955. (published) [Comet, SDSC, Stampede, TACC, Trestles] 84. TG-CTS120005 478. K. Maeda, W. Kreider, A. Maxwell, B. Cunitz, T. Colonius, M. Bailey, “Modeling and experimental analysis of acoustic cavitation bubbles for Burst Wave Lithotripsy," Journal of Physics: Conference Series, vol. 656, no. 1, 2015. 479. K. Maeda, T. Colonius, W. Kreider, A. Maxwell, “Modeling Cavitation Bubbles in a Focused Ultrasound Field," 9th Intl. Conf. on Multiphase Flow, ICMF 2016. Firenze, Italy, May 22 to 27, 2016, accepted for oral presentation. 85. TG-CTS130004 480. Agrawal, B., Sharma, A. 2016. Numerical Analysis of Aerodynamic Noise Mitigation via Leading Edge Serrations for a Rod-Airfoil Configuration. International Journal of Aeroacoustics. (accepted) [Stampede, TACC] 481. Chen, L., C. Harding, A. Sharma, and E. MacDonald (2016), Modeling noise and lease soft costs improves wind farm design and cost-of-energy predictions, Renewable Energy, 97, 849–859, doi:10.1016/j.renene.2016.05.045. (published) [Stampede, TACC] 482. Ju, H., R. Mani, M. Vysohlid, and A. Sharma (2015), Investigation of Fan-Wake/Outlet-Guide-Vane Interaction Broadband Noise, AIAA Journal, 53(12), 3534–3550, doi:10.2514/1.j053167. (published) [Stampede, TACC] 483. Rosenberg, A., and A. Sharma (2016), A Prescribed-Wake Vortex Lattice Method for Preliminary Design of Co- Axial, Dual-Rotor Wind Turbines, Journal of Solar Energy Engineering, 138(6), 061002, doi:10.1115/1.4034350. (published) [Stampede, TACC] 86. TG-CTS150005 484. Deshlahra, P., Iglesia, E. 2016. Reactivity and Selectivity Descriptors for the Activation of C-H Bonds in Hydrocarbons and Oxygenates on Metal Oxides. Journal of Physical Chemistry C. (accepted) [SDSC, TACC] 485. Deshlahra, P., Iglesia, E. 2016. Towards More Complete Reactivity Descriptors for Acid Catalysis. ACS Catalysis. (accepted) [TACC] 486. P. Deshlahra, R. Carr, S.-H. Chai and E. Iglesia*, “Mechanistic Details and Reactivity Descriptors for Acid- Oxidation Reactions of Methanol on Bifunctional Keggin Polyoxometalate Clusters,” ACS Catal. 2015, 5, 666- 682. 487. P. Deshlahra and E. Iglesia, “Methanol Oxidative Dehydrogenation on Oxide Catalysts: Molecular and Dissociative Routes and Hydrogen Addition Energies as Descriptors of Reactivity,” J. Phys. Chem. C. 2014, 118 (45), 26115-26129. 488. P. Deshlahra, R. Carr and E. Iglesia, “Ionic and Covalent Stabilization of Intermediates and Transition States in Catalysis by Solid Acids,” J. Am. Chem. Soc. 2014, 136 (43), 15229-15247. 489. P. Deshlahra, and E. Iglesia, “Mechanistic Details of Catalytic Redox Processes During Alkanol-O2 Reactions On Oxides: Experiment and Theory,” 24th International Symposia on Chemical Reaction Engineering, June 2016, Minneapolis. 490. P. Deshlahra, and E. Iglesia, “Ionic and Covalent Stabilization of Reactants and Transition States in Acid Catalysis,” 24th North American Catalysis Society Meeting, June 2015, Pittsburgh. 491. P. Deshlahra, N. Phadke and E. Iglesia, “Reactivity Predictors and Composition-Function Relations in Redox Catalysis by Metal Oxides,” 24th North American Catalysis Society Meeting, June 2015, Pittsburgh. 492. P. Deshlahra, R. Carr, N. Phadke and E. Iglesia, “Mechanistic Consequences of Composition and Descriptors of Reactivity for Alkanol ODH Reactions on Polyoxometalate Clusters,” AICHE Annual Meeting, November 2014, Atlanta. 493. P. Deshlahra and E. Iglesia, “Methanol Oxidation on Acid-Redox Bifunctional Polyoxometalate Clusters: Experiments and Theory,” ACS annual meeting, August 2014, San Francisco.
PY6 IPR 1 Page 113 87. TG-CTS150038 494. Baltrusaitis, J., T. Bučko, W. Michaels, M. Makkee, and G. Mul (2016), Catalytic methyl mercaptan coupling to ethylene in chabazite: DFT study of the first CC bond formation, Applied Catalysis B: Environmental, 187, 195–203, doi:10.1016/j.apcatb.2016.01.021. (published) 88. TG-CTS150039, TG-ENG140002 495. Sakievich, P. J., Y. T. Peet, and R. J. Adrian (2016), Large-scale thermal motions of turbulent Rayleigh–Bénard convection in a wide aspect-ratio cylindrical domain, International Journal of Heat and Fluid Flow, doi:10.1016/j.ijheatfluidflow.2016.04.011. (published) [Stampede, TACC] 89. TG-CTS150053 496. Biegert, E., Vowinckel, B., Meiburg, E. 2016. Grain-resolving simulations of flows over dense, mobile, multidisperse granular sediment beds: an Immersed Boundary approach. Journal of Computational Physics. (submitted) 497. Konopliv, N., Meiburg, E. 2016. Double-diffusive lock-exchange gravity currents. Journal of Fluid Mechanics 797: 729-764. http://dx.doi.org/10.1017/jfm.2016.300 (published) 90. TG-DDM160002 498. Bucher, T., Bolger, C., Zhang, M., Chen, C., and Yao, Y.L., 2016. Effect of Geometrical Modeling on Prediction of Laser-Induced Heat Transfer in Metal Foam. Proceedings of the 44th SME North American Manufacturing Research Conference, 30, 1-18. 499. Bucher, T., Bolger, C., Zhang, M., Chen, C., and Yao, Y.L., 2016. Effect of Geometrical Modeling on Prediction of Laser-Induced Heat Transfer in Metal Foam. Journal of Manufacturing Science and Engineering, doi:10.1115/1.4033927 91. TG-DEB140021 500. Stock, B. C., and B. X. Semmens (2016), Unifying error structures in commonly used biotracer mixing models, Ecology, 97(10), 2562–2569, doi:10.1002/ecy.1517. (published) [Gordon, SDSC] 501. Stock, B., Ward, E., Eguchi, T., Jannot, J., Forney, E., et al. 2015. Predicting Bycatch in Space: Comparison of Different Approaches. American Fisheries Society (Portland, OR). https://afs.confex.com/afs/2015/webprogram/Paper22678.html. (published) [Comet, SDSC] 92. TG-DMR090023 502. Jacobs, R., Booske, J., Morgan, D. 2016. Understanding and Controlling the Work Function of Perovskite Oxides Using Density Functional Theory. Advanced Functional Materials: 1-12. (published) [Stampede, TACC] 503. Ko, H., Deng, J., Szlufarska, I., Morgan, D. 2016. Ag diffusion in SiC high-energy grain boundaries: Kinetic Monte Carlo study with first-principle calculations. Computational Materials Science 121: 248--257. (published) [Stampede, TACC] 504. Li, L., Jacobs, R., Gao, P., Gan, L., Wang, F., et al. 2016. Origins of Large Voltage Hysteresis in High-Energy- Density Metal Fluoride Lithium-Ion Battery Conversion Electrodes. Journal of the American Chemical Society 138: 2838--2848. (published) [Stampede, TACC] 505. Luo, G., Yang, S., Li, J., Arjmand, M., Szlufarska, I., et al. 2015. First-principles studies on molecular beam epitaxy growth of GaA s 1- x B i x. Physical Review B 92: 035415. (published) [Stampede, TACC] 506. Wu, H., Mayeshiba, T., Morgan, D. 2016. High-throughput ab-initio dilute solute diffusion database. Scientific Data . (accepted) [Stampede, TACC] 507. Xie, W., Chang, Y., Morgan, D. 2016. Ab initio energetics for modeling phase stability of the Np-U system. Journal of nuclear materials . (accepted) [Stampede, TACC] 508. Xu, S., Jacobs, R., Nguyen, H., Hao, S., Mahanthappa, M., et al. 2015. Lithium transport through lithium-ion battery cathode coatings. Journal of Materials Chemistry A 3: 17248--17272. (published) [Stampede, TACC] 509. Xu, S., Shim, S., Morgan, D. 2015. Origin of Fe 3+ in Fe-containing, Al-free mantle silicate perovskite. Earth and Planetary Science Letters 409: 319--328. (published) [Stampede, TACC] 510. Zheng, M., Szlufarska, I., Morgan, D. 2016. Ab initio prediction of threshold displacement energies in ZrC. Journal of Nuclear Materials 471: 214--219. (published) [Stampede, TACC]
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PY6 IPR 1 Page 123 682. Chen, M., Niu, F., Liu, Q., and Tromp, J. (2016), Multiparameter adjoint tomography of the crust and upper mantle beneath East Asia: 2. Lithospheric structure, in preparation. 683. Chen, M. and Kiser, E. (2016), A reappraisal of mechanisms causing deep-focus earthquakes in Western Pacific Subduction Zones inferred from structural heterogeneity, in preparation. 684. Liu, Y., Chen, M., Niu, F., Yang, W., and Xing, G. (2016), 3-D crustal and uppermost mantle structure of northeast China based on adjoint tomography using ambient noise derived Green’s functions, in preparation. 685. Masy, J., Chen, M., Niu, F. and Levander, A. (2016), High-resolution adjoint tomography of the crust beneath the Eastern Venezuela using empirical Green’s function data from ambient noise, to be submitted to Earth Planet. Sci. Lett. 686. Chen, M., Niu, F., Tromp, J., Lenardic, A., Lee, C.-T. A., Cao, W., and Ribeiro, J. (2016), Lithospheric foundering and underthrusting imaged beneath Tibet, submitted. 687. Xing, G., Niu, F., Chen, M., Yang, Y. (2016), Effects of shallow density structure on the inversion for crustal shear wavespeeds in surface wave tomography, Geophysical Journal International, doi: 10.1093/gji/ggw064. 688. Chen, M., Niu, F., Liu, Q., and Tromp, J. (2015), Mantle-driven uplift of Hangai Dome: New seismic constraints from adjoint tomography, Geophys. Res. Lett., 42, doi:10.1002/2015GL065018. 689. Chen, M., Niu, F., Liu, Q., Tromp, J. and Zheng, X. (2015), Multiparameter adjoint tomography of the crust and upper mantle beneath East Asia: 1. Model construction and comparisons, J. Geophys. Res., doi:10.1002/2014JB011638. 690. Chen, M., Huang, H., Yao, H., van der Hilst, R. D. and Niu, F. (2014), Low wave speed zones in the crust beneath the SE Tibet revealed by ambient noise adjoint tomography, Geophys. Res. Lett., Vol. 41, No.2, 334-340, doi:10.1002/2013GL058476. 127. TG-ECS150005 691. Li, Y., S. He, A. Russakoff, and K. Varga (2016), Accurate time propagation method for the coupled Maxwell and Kohn-Sham equations, Physical Review E, 94(2), doi:10.1103/physreve.94.023314. (published) [Comet, SDSC] 128. TG-ENG140004 692. Durbin, P., Yin, Z., Jeyapaul, E. 2016. Adaptive Detached-Eddy Simulation of Three-Dimensional Diffusers. Journal of Fluids Engineering 138: 101201. (published) [Stampede, TACC] 693. Yin, Z., Durbin, P. 2016. Passive Scalar Transport Modeling for Hybrid RANS/LES Simulation. Flow, Turbulence and Combustion: 1--18. (published) [Stampede, TACC] 694. Yin, Z., Durbin, P. 2016. An adaptive DES smodel that allows wall-resolved eddy simulation. International Journal of Heat and Fluid Flow. (published) [Stampede, TACC] 129. TG-ENG150034 695. Chung, J., I. Granja, M. G. Taylor, G. Mpourmpakis, J. R. Asplin, and J. D. Rimer (2016), Molecular modifiers reveal a mechanism of pathological crystal growth inhibition, Nature, 536(7617), 446–450, doi:10.1038/nature19062. (published) [Gordon] 130. TG-ENG160012, TG-MSS150019 696. Kawamoto, R., Andrade, J. 2016. Simulation of Triaxial Shear Localization Using LS-DEM. Presentation given at Engineering Mechanics Institute conference, May 24, 2016. (published) [Comet, SDSC, Stampede, TACC] 131. TG-IBN130007 697. Albert, M., Soldan, A., Selnes, O., Miller, M., Ratnanather, T., et al. 2015. Using combinations of variables to identify individuals with preclinical ad. Alzheimer’s & Dementia 11: P118. (published) 132. TG-MCA00N020 698. Klein, R., Li, P., McKee, C. 2015. Multi-Physics Feedback Simulations with Realistic Initial Conditions of the Formation of Star Clusters: From Large Scale Magnetized Clouds to Turbulent Clumps to Cores to Stars. Numerical Modeling of Space Plasma Flows ASTRONUM-2014. 498. 91. (published) 699. Lee, A. T., A. J. Cunningham, C. F. McKee, and R. I. Klein (2014), BONDI-HOYLE ACCRETION IN AN ISOTHERMAL MAGNETIZED PLASMA, The Astrophysical Journal, 783(1), 50, doi:10.1088/0004- 637x/783/1/50. (published) [Ranch, Stampede, TACC]
PY6 IPR 1 Page 124 133. TG-MCA01S027, TG-MCA01T027 700. Bergonzo, C., and T. E. Cheatham (2015), Improved Force Field Parameters Lead to a Better Description of RNA Structure, Journal of Chemical Theory and Computation, 11(9), 3969–3972, doi:10.1021/acs.jctc.5b00444. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 701. Bergonzo, C., K. B. Hall, and T. E. Cheatham (2015), Stem-Loop V of Varkud Satellite RNA Exhibits Characteristics of the Mg 2+ Bound Structure in the Presence of Monovalent Ions , J. Phys. Chem. B, 119(38), 12355–12364, doi:10.1021/acs.jpcb.5b05190. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 702. Bergonzo, C., K. B. Hall, and T. E. Cheatham (2016), Divalent Ion Dependent Conformational Changes in an RNA Stem-Loop Observed by Molecular Dynamics, Journal of Chemical Theory and Computation, 12(7), 3382– 3389, doi:10.1021/acs.jctc.6b00173. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 703. Bergonzo, C., Roe, D., Henriksen, N., Cheatham, T. 2015. Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields. RNA 29: 1578-1590. doi: 10.1261/rna.051102.115 (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 704. Cheatham, T. E., and D. R. Roe (2015), The Impact of Heterogeneous Computing on Workflows for Biomolecular Simulation and Analysis, Computing in Science & Engineering, 17(2), 30–39, doi:10.1109/mcse.2015.7. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 705. Galindo-Murillo, R., D. R. Davis, and T. E. Cheatham (2016), Probing the influence of hypermodified residues within the tRNA3Lys anticodon stem loop interacting with the A-loop primer sequence from HIV-1, Biochimica et Biophysica Acta (BBA) - General Subjects, 1860(3), 607–617, doi:10.1016/j.bbagen.2015.11.009. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 706. Galindo-Murillo, R., J. C. Robertson, M. Zgarbová, J. Šponer, M. Otyepka, P. Jurečka, and T. E. Cheatham (2016), Assessing the Current State of Amber Force Field Modifications for DNA, Journal of Chemical Theory and Computation, 12(8), 4114–4127, doi:10.1021/acs.jctc.6b00186. (published) [Comet, GaTech, Globus Online, IU, Keenland, NICS, OSG, PSC, SDSC, Stampede, TACC] 707. Heidari, Z., D. R. Roe, R. Galindo-Murillo, J. B. Ghasemi, and T. E. Cheatham (2016), Using Wavelet Analysis To Assist in Identification of Significant Events in Molecular Dynamics Simulations, J. Chem. Inf. Model., 56(7), 1282–1291, doi:10.1021/acs.jcim.5b00727. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 708. Robertson, J. C., and T. E. Cheatham (2015), DNA Backbone BI/BII Distribution and Dynamics in E2 Protein- Bound Environment Determined by Molecular Dynamics Simulations, J. Phys. Chem. B, 119(44), 14111– 14119, doi:10.1021/acs.jpcb.5b08486. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 709. Simmonett, A. C., F. C. Pickard, Y. Shao, T. E. Cheatham, and B. R. Brooks (2015), Efficient treatment of induced dipoles, J. Chem. Phys., 143(7), 074115, doi:10.1063/1.4928530. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 710. Thibault, J. C., D. R. Roe, K. Eilbeck, T. E. Cheatham III, and J. C. Facelli (2015), Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology, SAR and QSAR in Environmental Research, 26(7-9), 577–593, doi:10.1080/1062936x.2015.1076515. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 711. Zgarbová, M., J. Šponer, M. Otyepka, T. E. Cheatham, R. Galindo-Murillo, and P. Jurečka (2015), Refinement of the Sugar–Phosphate Backbone Torsion Beta for AMBER Force Fields Improves the Description of Z- and B- DNA, Journal of Chemical Theory and Computation, 11(12), 5723–5736, doi:10.1021/acs.jctc.5b00716. (published) [Blacklight, Comet, GaTech, Globus Online, Keenland, NICS, PSC, SDSC, Stampede, TACC] 134. TG-MCA03S027 712. Earnest, T. M., J. A. Cole, J. R. Peterson, M. J. Hallock, T. E. Kuhlman, and Z. Luthey-Schulten (2016), Ribosome biogenesis in replicating cells: Integration of experiment and theory, Biopolymers, 105(10), 735–751, doi:10.1002/bip.22892. (published) 713. Earnest, T. M., J. Lai, K. Chen, M. J. Hallock, J. R. Williamson, and Z. Luthey-Schulten (2015), Toward a Whole- Cell Model of Ribosome Biogenesis: Kinetic Modeling of SSU Assembly, Biophysical Journal, 109(6), 1117– 1135, doi:10.1016/j.bpj.2015.07.030. (published)
PY6 IPR 1 Page 125 714. Hallock, M., Luthey-Schulten, Z. 2016. Improving reaction kernel performance in Lattice Microbes: particle- wise propensities and run-time generated code. 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (Chicago, Illinois). (published) 715. Peterson, J. R., J. A. Cole, J. Fei, T. Ha, and Z. A. Luthey-Schulten (2015), Effects of DNA replication on mRNA noise, Proc Natl Acad Sci USA, 112(52), 15886–15891, doi:10.1073/pnas.1516246112. (published) 716. Z., G., Guzman, I., J. Baek, M., Luthey-Schulten, Z. 2016. Estimation of relative protein-RNA binding strengths from fluctuations of the bound state. J. Chem. Theory Comput.. (submitted) 135. TG-MCA05S027 717. Das, S., Lee, B., Lindstadt, R., Cunha, K., Li, Y., et al. 2016. Robust and Rapid Defect-Free Self-Assembled Monomolecular Layer for High Performance Organic Electronics. Nature Communications. (submitted) 718. De Almeida, N. E. C., T. D. Do, M. Tro, N. E. LaPointe, S. C. Feinstein, J.-E. Shea, and M. T. Bowers (2016), Opposing Effects of Cucurbit[7]uril and 1,2,3,4,6-Penta- O -galloyl-β- d -glucopyranose on Amyloid β 25–35 Assembly , ACS Chemical Neuroscience, 7(2), 218–226, doi:10.1021/acschemneuro.5b00280. (published) 719. Do, T. D. et al. (2016), Amyloid β-Protein C-Terminal Fragments: Formation of Cylindrins and β-Barrels, Journal of the American Chemical Society, 138(2), 549–557, doi:10.1021/jacs.5b09536. (published) 720. Ganguly, P., van der Vegt, N., Shea, J. 2016. Hydrophobic Association in Mixed Urea-TMAO Solutions. Journal of Physical Chemistry Letters. (submitted) 721. Ilitchev, A. I., M. J. Giammona, T. D. Do, A. G. Wong, S. K. Buratto, J.-E. Shea, D. P. Raleigh, and M. T. Bowers (2016), Human Islet Amyloid Polypeptide N-Terminus Fragment Self-Assembly: Effect of Conserved Disulfide Bond on Aggregation Propensity, Journal of The American Society for Mass Spectrometry, 27(6), 1010–1018, doi:10.1007/s13361-016-1347-7. (published) 722. Kim, B., Do, T., Hayden, E., Teplow, D., Bowers, M., et al. 2016. Aggregation of Chameleon Peptides: Implications of α-Helicity in Fibril Formation. Journal of Physical Chemistry B. (accepted) 723. Levine, Z. A., S. A. Fischer, J.-E. Shea, and J. Pfaendtner (2015), Trp-Cage Folding on Organic Surfaces, J. Phys. Chem. B, 119(33), 10417–10425, doi:10.1021/acs.jpcb.5b04213. (published) 724. Levine, Z. A., M. V. Rapp, W. Wei, R. G. Mullen, C. Wu, G. H. Zerze, J. Mittal, J. H. Waite, J. N. Israelachvili, and J.-E. Shea (2016), Surface force measurements and simulations of mussel-derived peptide adhesives on wet organic surfaces, Proc Natl Acad Sci USA, 113(16), 4332–4337, doi:10.1073/pnas.1603065113. (published) 725. Peter, E., Pivkin, I., Shea, J. 2016. A Constant Pressure Replica Exchange Molecular Dynamics Technique in the NV(p)T Ensemble. Journal of Chemical Physics. (accepted) 726. Shea, J.-E., and Z. A. Levine (2016), Studying the Early Stages of Protein Aggregation Using Replica Exchange Molecular Dynamics Simulations, Protein Amyloid Aggregation, 225–250, doi:10.1007/978-1-4939-2978- 8_15. (published) 727. Wong, A. G., C. Wu, E. Hannaberry, M. D. Watson, J.-E. Shea, and D. P. Raleigh (2016), Analysis of the Amyloidogenic Potential of Pufferfish ( Takifugu rubripes ) Islet Amyloid Polypeptide Highlights the Limitations of Thioflavin-T Assays and the Difficulties in Defining Amyloidogenicity , Biochemistry, 55(3), 510–518, doi:10.1021/acs.biochem.5b01107. (published) 728. Zerze, G. H., R. G. Mullen, Z. A. Levine, J.-E. Shea, and J. Mittal (2015), To What Extent Does Surface Hydrophobicity Dictate Peptide Folding and Stability near Surfaces?, Langmuir, 31(44), 12223–12230, doi:10.1021/acs.langmuir.5b03814. (published) 729. Zheng, X., C. Wu, D. Liu, H. Li, G. Bitan, J.-E. Shea, and M. T. Bowers (2016), Mechanism of C-Terminal Fragments of Amyloid β-Protein as Aβ Inhibitors: Do C-Terminal Interactions Play a Key Role in Their Inhibitory Activity?, J. Phys. Chem. B, 120(8), 1615–1623, doi:10.1021/acs.jpcb.5b08177. (published) 136. TG-MCA07S015 730. Miyawaki, S., S. Choi, E. A. Hoffman, and C.-L. Lin (2016), A 4DCT imaging-based breathing lung model with relative hysteresis, Journal of Computational Physics, 326, 76–90, doi:10.1016/j.jcp.2016.08.039. (published) [Comet, Data Oasis, Gordon, Ranch, SDSC, Stampede, TACC] 731. Miyawaki, S., E. A. Hoffman, and C.-L. Lin (2016), Effect of static vs. dynamic imaging on particle transport in CT-based numerical models of human central airways, Journal of Aerosol Science, 100, 129–139, doi:10.1016/j.jaerosci.2016.07.006. (published) [Comet, Data Oasis, Gordon, Ranch, SDSC, Stampede, TACC]
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138. TG-MCA08X014 739. Cole, D. K. et al. (2016), Hotspot autoimmune T cell receptor binding underlies pathogen and insulin peptide cross-reactivity, Journal of Clinical Investigation, 126(6), 2191–2204, doi:10.1172/jci85679. (published) 740. Hood, J. D., V. I. Zarnitsyna, C. Zhu, and B. D. Evavold (2015), Regulatory and T Effector Cells Have Overlapping Low to High Ranges in TCR Affinities for Self during Demyelinating Disease, The Journal of Immunology, 195(9), 4162–4170, doi:10.4049/jimmunol.1501464. (published) 741. Ju, L., J. Lou, Y. Chen, Z. Li, and C. Zhu (2015), Force-Induced Unfolding of Leucine-Rich Repeats of Glycoprotein Ibα Strengthens Ligand Interaction, Biophysical Journal, 109(9), 1781–1784, doi:10.1016/j.bpj.2015.08.050. (published) 139. TG-MCA09X001 742. G. Luo, S. Yang, J. Li, M. Arjmand, I. Szlufarska, A. Brown, T. Keuch, D. Morgan, Firstprinciples Studies on Molecular Beam Epitaxy Growth of GaAs1-xBix, Physical Review B 92, 035415 (2015). 743. H. Nam, D. Morgan, Redox condition in molten salts and solute behavior: A first-principles molecular dynamics study, Journal of Nuclear Materials 465, p. 224-235 (2015). 744. S. Xu, R. M. Jacobs, H. M. Nguyen, S. Hao, M. Mahanthappa, C. Wolverton, and D. Morgan, Lithium transport through lithium-ion battery cathode coatings, Journal of Materials Chemistry A 3, p. 17248-17272 (2015) 745. M. J. Zheng, I. Szlufarska, and D. Morgan, Ab initio prediction of threshold displacement energies in ZrC, Journal of Nuclear Materials 471, p. 214-219 (2016). 746. R. Jacobs, J. Booske, D. Morgan, Understanding and Controlling the Work Function of Perovskite Oxides Using Density Functional Theory, Advanced Functional Materials, 1-12 (2016). 747. H. Ko, J. Deng, I. Szlufarska, D. Morgan, Ag diffusion in SiC high-energy grain boundaries: Kinetic Monte Carlo study with first-principle calculations, Comput. Mater. Sci. 121 (2016) 248- 257. 748. L. Li, R. Jacobs, P. Gao, L. Gan, F. Wang, D. Morgan, and S. Jin, Origins of Large Voltage Hysteresis in High- Energy-Density Metal Fluoride Lithium-Ion Battery Conversion Electrodes, J Am Chem Soc 138, p. 2838–2848 (2016). 749. W. Xie, Y.-L. Lee, Y. Shao-Horn, and D. Morgan, Oxygen Point Defect Chemistry in Ruddlesden-Popper Oxides (La1-xSrx)(2)MO4 +/-delta (M = Co, Ni, Cu), Journal of Physical Chemistry Letters 7, p. 1939-1944 (2016). 750. S. Z. Xu, S. H. Shim, and D. Morgan, Origin of Fe3+ in Fe-containing, Al-free mantle silicate perovskite, Earth and Planetary Science Letters 409, p. 319-328 (2015). 751. H. Wu, T. Mayeshiba, D. Morgan, High-throughput ab-initio dilute solute diffusion database, Scientific Data (2016, in press). 752. W. Xie, Y. A. Chang, and D. Morgan, Ab initio energetics for modeling phase stability of the Np-U system, Journal of Nuclear Materials (2016, accepted for publication). 753. Shenzhen Xu, Jung-Fu Lin, Dane Morgan, Iron Speciation Induced Chemical and Seismic Heterogeneities in the Lower Mantle, Submitted toProceedings of National Academy of Sciences (2016). 754. Tam Mayeshiba and D. Morgan, Factors Controlling Oxygen Migration Barriers in Perovskites, Submitted to Solid State Ionics (2016).
PY6 IPR 1 Page 127 140. TG-MCA09X003 755. Green, S. R., A. Maillard, L. Lehner, and S. L. Liebling (2015), Islands of stability and recurrence times in AdS, Phys. Rev. D, 92(8), doi:10.1103/physrevd.92.084001. (published) [Stampede, TACC] 756. Palenzuela, C., and S. L. Liebling (2016), Constraining scalar-tensor theories of gravity from the most massive neutron stars, Phys. Rev. D, 93(4), doi:10.1103/physrevd.93.044009. (published) [TACC] 757. G. Khanna, S.L. Liebling, “Scalar Collapse in AdS with an OpenCL, open source code,” In preparation (Performance results shown in Fig. 6 are from this work). 758. S.L. Liebling, C. Palenzuela, “Electromagnetic Luminosity of the Coalescence of Charged Black Hole Binaries,” (2016). gr-qc/1607.02140 759. L. Lehner, S.L. Liebling, C. Palenzuela, P. Motl, “The m = 1 instability & gravitational wave signal in binary neutron star mergers,” (2016). gr-qc/1605.02369 760. L. Lehner, S.L. Liebling, C. Palenzuela, O.L. Caballero, E. O’Connor,M. Anderson, D. Neilsen, “Unequal mass binary neutron star mergers and multimessenger signals,” gr-qc/1603.00501 761. C. Palenzuela, S.L. Liebling, D. Neilsen, L. Lehner, O.L. Caballero, E. O’Connor, M. Anderson, “Effects of the microphysical Equation of State in the mergers of magnetized Neutron StarsWith Neutrino Cooling,” Physical Review D 92 044045 (2015). gr-qc/1505.01607 Featured in the Kaleidoscope section of PRD. 762. J. DeBuhr, B. Zhang, M. Anderson, D. Neilsen and E. W. Hirschmann, “Relativistic Hydrodynamics with Wavelets,” arXiv:1512.00386 [astro-ph.IM]. 763. F.W. Glines,M. Anderson and D. Neilsen, “Scalable relativistic high-resolution shockcapturing for heterogeneous computing,” submitted to the IEEE Workshop Series on Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA) (2015). 141. TG-MCA93S001 764. Chen, C., A. Esadze, L. Zandarashvili, D. Nguyen, B. M. Pettitt, and J. Iwahara (2015), Dynamic Equilibria of Short-Range Electrostatic Interactions at Molecular Interfaces of Protein–DNA Complexes, The Journal of Physical Chemistry Letters, 6(14), 2733–2737, doi:10.1021/acs.jpclett.5b01134. (published) 765. Chen, C., and B. M. Pettitt (2016), DNA Shape versus Sequence Variations in the Protein Binding Process, Biophysical Journal, 110(3), 534–544, doi:10.1016/j.bpj.2015.11.3527. (published) 766. Esadze, A., C. Chen, L. Zandarashvili, S. Roy, B. M. Pettitt, and J. Iwahara (2016), Changes in conformational dynamics of basic side chains upon protein–DNA association, Nucleic Acids Research, 44(14), 6961–6970, doi:10.1093/nar/gkw531. (published) 767. Harris, R. C., and B. M. Pettitt (2015), Examining the Assumptions Underlying Continuum-Solvent Models, Journal of Chemical Theory and Computation, 11(10), 4593–4600, doi:10.1021/acs.jctc.5b00684. (published) 768. Karandur, D., R. C. Harris, and B. M. Pettitt (2015), Protein collapse driven against solvation free energy without H-bonds, Protein Science, 25(1), 103–110, doi:10.1002/pro.2749. (published) 769. Nguyen, B. L., and B. M. Pettitt (2015), Effects of Acids, Bases, and Heteroatoms on Proximal Radial Distribution Functions for Proteins, Journal of Chemical Theory and Computation, 11(4), 1399–1409, doi:10.1021/ct501116v. (published) 770. Wang, Q., and B. M. Pettitt (2016), Sequence Affects the Cyclization of DNA Minicircles, The Journal of Physical Chemistry Letters, 7(6), 1042–1046, doi:10.1021/acs.jpclett.6b00246. (published) 142. TG-MCA93S002 771. Basak, S., Bazavov, A., Bernard, C., DeTar, C., Freeland, E., et al. 2015. Electromagnetic effects on the light pseudoscalar mesons and determination of m_u/m_d. The 33nd International Symposium on Lattice Field Theory (LATTICE 2015) (Kobe, Japan). Proceedings of Science (LATTICE 2015). 259. http://arxiv.org/pdf/1606.01228.pdf. (published) [Globus Online, Keenland, Lonestar, NICS, PSC, Ranch, Stampede, TACC] 772. Basak, S. et al. (2015), Electromagnetic effects on the light hadron spectrum, Journal of Physics: Conference Series, 640, 012052, doi:10.1088/1742-6596/640/1/012052. (published) [Globus Online, Keenland, NICS, Ranch, Stampede, TACC] 773. Bazavov, A., Bernard, C., Bouchard, C., Brown, N., DeTar, C., et al. 2015. Decay constants f_B and f_{B_s} from HISQ simulations. The 33nd International Symposium on Lattice Field Theory (LATTICE 2015) (Kobe, Japan). Proceedings of Science (LATTICE 2015). 331. http://arxiv.org/pdf/1511.02294.pdf. (published) [Globus Online, Keenland, Ranch, Stampede]
PY6 IPR 1 Page 128 774. Bazavov, A. et al. (2016), B(s)0-mixing matrix elements from lattice QCD for the Standard Model and beyond, Phys. Rev. D, 93(11), doi:10.1103/physrevd.93.113016. (published) [Globus Online, Lonestar, NICS, Ranch, Stampede, TACC] 143. TG-MCA93S005 775. Augustson, K., Brun, A., Miesch, M., Toomre, J. 2015. Grand Minima and Equatorward Propagation in a Cycling Convective Dynamo. Astrophysical Journal 809: 149. (published) [NICS, Ranch, Stampede, TACC] 776. Nelson, N., Miesch, M. 2014. Generating Buoyant Magnetic Flux Ropes in Solar-Like Convective Dynamos. Plasma Physics and Controlled Fusion 56: 064004. (published) [NICS, Ranch, SDSC, Stampede, TACC] 144. TG-MCA93S013 777. Caliman, A. D., S. E. Swift, Y. Wang, Y. Miao, and J. A. McCammon (2015), Investigation of the conformational dynamics of the apo A2Aadenosine receptor, Protein Science, 24(6), 1004–1012, doi:10.1002/pro.2681. (published) [Comet, Gordon, SDSC] 778. Cheng, Y., V. Rao, A. Tu, S. Lindert, D. Wang, L. Oxenford, A. D. McCulloch, J. A. McCammon, and M. Regnier (2015), Troponin I Mutations R146G and R21C Alter Cardiac Troponin Function, Contractile Properties and Modulation by PKA-mediated Phosphorylation, J. Biol. Chem., jbc.M115.683045, doi:10.1074/jbc.m115.683045. (published) [Comet, Gordon, SDSC] 779. Kappel, K., Y. Miao, and J. A. McCammon (2015), Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor, Quart. Rev. Biophys., 48(04), 479–487, doi:10.1017/s0033583515000153. (published) [Comet, Gordon, SDSC] 780. Kim, M. O., P. G. Blachly, and J. A. McCammon (2015), Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria, edited by D. R. Livesay, PLoS Comput Biol, 11(10), e1004341, doi:10.1371/journal.pcbi.1004341. (published) [Comet, Gordon, SDSC] 781. Kim, O., McCammon, J. 2016. Computation of pH-dependent Binding Free Energies.. Biopolymers 105: 43-49. (published) [Comet, Gordon, SDSC] 782. Lindert, S., Y. Cheng, P. Kekenes-Huskey, M. Regnier, and J. A. McCammon (2015), Effects of HCM cTnI Mutation R145G on Troponin Structure and Modulation by PKA Phosphorylation Elucidated by Molecular Dynamics Simulations, Biophysical Journal, 108(2), 395–407, doi:10.1016/j.bpj.2014.11.3461. (published) [Comet, Gordon, SDSC] 783. Miao, Y., A. D. Caliman, and J. A. McCammon (2015), Allosteric Effects of Sodium Ion Binding on Activation of the M3 Muscarinic G-Protein-Coupled Receptor, Biophysical Journal, 108(7), 1796–1806, doi:10.1016/j.bpj.2015.03.003. (published) 784. Miao, Y., V. A. Feher, and J. A. McCammon (2015), Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation, Journal of Chemical Theory and Computation, 11(8), 3584– 3595, doi:10.1021/acs.jctc.5b00436. (published) [Comet, Gordon, SDSC] 785. Miao, Y., D. A. Goldfeld, E. V. Moo, P. M. Sexton, A. Christopoulos, J. A. McCammon, and C. Valant (2016), Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein- coupled receptor, Proceedings of the National Academy of Sciences, 113(38), E5675–E5684, doi:10.1073/pnas.1612353113. (published) [Comet, Gordon, SDSC] 786. Miao, Y., and J. A. McCammon (2016), G-protein coupled receptors: advances in simulation and drug discovery, Current Opinion in Structural Biology, 41, 83–89, doi:10.1016/j.sbi.2016.06.008. (published) [Comet, Gordon, SDSC] 787. Miao, Y., and J. A. McCammon (2016), Unconstrained enhanced sampling for free energy calculations of biomolecules: a review, Molecular Simulation, 42(13), 1046–1055, doi:10.1080/08927022.2015.1121541. (published) [Comet, Gordon, SDSC] 145. TG-MCA95C006 788. Hu, X.-M., Z. Ma, W. Lin, H. Zhang, J. Hu, Y. Wang, X. Xu, J. D. Fuentes, and M. Xue (2014), Impact of the Loess Plateau on the atmospheric boundary layer structure and air quality in the North China Plain: A case study, Science of The Total Environment, 499, 228–237, doi:10.1016/j.scitotenv.2014.08.053. (published) [Stampede, TACC] 789. Qi, Y., J. Zhang, Q. Cao, Y. Hong, and X.-M. Hu (2013), Correction of Radar QPE Errors for Nonuniform VPRs in Mesoscale Convective Systems Using TRMM Observations, Journal of Hydrometeorology, 14(5), 1672–1682, doi:10.1175/jhm-d-12-0165.1. (published) [Stampede, TACC]
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PY6 IPR 1 Page 130 810. Paz, S. A., and C. F. Abrams (2015), Free Energy and Hidden Barriers of the β-Sheet Structure of Prion Protein, Journal of Chemical Theory and Computation, 11(10), 5024–5034, doi:10.1021/acs.jctc.5b00576. (published) [Stampede, TACC] 811. Sharifi, M., C. Jang, C. F. Abrams, and G. R. Palmese (2015), Epoxy Polymer Networks with Improved Thermal and Mechanical Properties via Controlled Dispersion of Reactive Toughening Agents, Macromolecules, 48(20), 7495–7502, doi:10.1021/acs.macromol.5b00677. (published) [Stampede, TACC] 149. TG-MCB080011 812. Adelman JL & Grabe M (2015). Simulating current-voltage relationships for a single-file ion channel using the weighted ensemble method. J. Comp. Theory Chem. 11: 1907-1918 813. Adelman, JL, C Ghezzi, P Bisignano, DDF Loo, S Choe, J Abramson, JM Rosenberg, EM Wright, & M Grabe (2016). Stochastic steps in secondary active sugar transport. Proc. Natl. Acad. Sci. USA 113 (27) E3960-E3966 814. Zwier, MC, JL Adelman, JW Kaus, AJ Pratt, KF Wong, NB Rego, E Suarez, S Letteri, DW Wang, M Grabe, DM Zuckerman, & LT Chong (2015). WESTPA: A portable, highly scalable software package for weighted ensemble simulation and analysis. J. Comp. Theory Chem. 11: 800-809 815. Ulas, G, T Lemmin, Y Wu, GT Gassner, & WF DeGrado (2016). Designed metalloprotein stabilizes a semiquinone radical Nature Chemistry 8, 354–359 (2016) 150. TG-MCB080026N 816. Ficici, E., Andricioaei, I. 2015. On the Possibility of Facilitated Diffusion of Dendrimers Along DNA. J Phys Chem B 119: 6894--6904. (published) [Stampede] 817. Ficici, E., Andricioaei, I., Howorka, S. 2015. Dendrimers in Nanoscale Confinement: The Interplay between Conformational Change and Nanopore Entrance. Nano Lett. 15: 4822--4828. (published) [Stampede] 818. Grazioli, G., Andricioaei, I. 2015. Advancements in Milestoning I: Accelerated Milestoning via "Wind" Assisted Re-weighted Milestoning (WARM). arXiv:1511.00044 [cond-mat.stat-mech]. (published) [Stampede] 819. Grazioli, G., Andricioaei, I. 2015. Advancements in Milestoning II: Calculating Autocorrelation from Milestoning Data Using Stochastic Path Integrals in Milestone Space. arXiv:1511.00045 [cond-mat.stat-mech]. (published) [Stampede] 820. Taranova, M., Hirsh, A., Perkins, N., Andricioaei, I. 2014. Role of microscopic flexibility in tightly curved DNA. J Phys Chem B 118: 11028--11036. (published) [Stampede] 151. TG-MCB080029N 821. Adelman, J. L., and M. Grabe (2015), Simulating Current–Voltage Relationships for a Narrow Ion Channel Using the Weighted Ensemble Method, Journal of Chemical Theory and Computation, 11(4), 1907–1918, doi:10.1021/ct501134s. (published) [Stampede, TACC] 822. Ulas, G., T. Lemmin, Y. Wu, G. T. Gassner, and W. F. DeGrado (2016), Designed metalloprotein stabilizes a semiquinone radical, Nature Chem, 8(4), 354–359, doi:10.1038/nchem.2453. (published) [Stampede, TACC] 152. TG-MCB080132N, TG-MCB090176T 823. Xu, J., Chen, B., Callis, P., Muiño, P., Rozeboom, H., et al. 2015. Picosecond Fluorescence Dynamics of Tryptophan and 5-Fluorotryptophan in Monellin: Slow Water−Protein Relaxation Unmasked. J. Phys. Chem. B 119: 4230−4239. (published) 153. TG-MCB090159 824. Berstis, L., G. T. Beckham, and M. F. Crowley (2015), Electronic coupling through natural amino acids, J. Chem. Phys., 143(22), 225102, doi:10.1063/1.4936588. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 825. Berstis, L., T. Elder, M. Crowley, and G. T. Beckham (2016), Radical Nature of C-Lignin, ACS Sustainable Chemistry & Engineering, 4(10), 5327–5335, doi:10.1021/acssuschemeng.6b00520. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 826. Borisova, A. S. et al. (2015), Sequencing, biochemical characterization, crystal structure and molecular dynamics of cellobiohydrolase Cel7A fromGeotrichum candidum3C, FEBS Journal, 282(23), 4515–4537, doi:10.1111/febs.13509. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 827. Borisova, A. S. et al. (2015), Structural and Functional Characterization of a Lytic Polysaccharide Monooxygenase with Broad Substrate Specificity, J. Biol. Chem., 290(38), 22955–22969, doi:10.1074/jbc.m115.660183. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC]
PY6 IPR 1 Page 131 828. Elder, T., L. Berstis, G. T. Beckham, and M. F. Crowley (2016), Coupling and Reactions of 5-Hydroxyconiferyl Alcohol in Lignin Formation, Journal of Agricultural and Food Chemistry, 64(23), 4742–4750, doi:10.1021/acs.jafc.6b02234. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 829. Ferguson, G. A., L. Cheng, L. Bu, S. Kim, D. J. Robichaud, M. R. Nimlos, L. A. Curtiss, and G. T. Beckham (2015), Carbocation Stability in H-ZSM5 at High Temperature, The Journal of Physical Chemistry A, 119(46), 11397– 11405, doi:10.1021/acs.jpca.5b07025. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 830. Geronimo, I., C. A. Denning, W. E. Rogers, T. Othman, T. Huxford, D. K. Heidary, E. C. Glazer, and C. M. Payne (2016), Effect of Mutation and Substrate Binding on the Stability of Cytochrome P450 BM3 Variants , Biochemistry, 55(25), 3594–3606, doi:10.1021/acs.biochem.6b00183. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 831. Griffin, M. B., G. A. Ferguson, D. A. Ruddy, M. J. Biddy, G. T. Beckham, and J. A. Schaidle (2016), Role of the Support and Reaction Conditions on the Vapor-Phase Deoxygenation of m -Cresol over Pt/C and Pt/TiO 2 Catalysts , ACS Catalysis, 6(4), 2715–2727, doi:10.1021/acscatal.5b02868. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 832. Hamre, A. G., S. Jana, M. M. Holen, G. Mathiesen, P. Väljamäe, C. M. Payne, and M. Sørlie (2015), Thermodynamic Relationships with Processivity in Serratia marcescens Family 18 Chitinases , J. Phys. Chem. B, 119(30), 9601–9613, doi:10.1021/acs.jpcb.5b03817. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 833. Hamre, A. G., S. Jana, N. K. Reppert, C. M. Payne, and M. Sørlie (2015), Processivity, Substrate Positioning, and Binding: The Role of Polar Residues in a Family 18 Glycoside Hydrolase, Biochemistry, 54(49), 7292–7306, doi:10.1021/acs.biochem.5b00830. (published) [Comet, Gordon, NICS, SDSC, Stampede, TACC] 834. Happs, R. M., X. Guan, M. G. Resch, M. F. 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(accepted) [Maverick, NICS, SDSC, TACC, Trestles] 844. Perlmutter, J. D., F. Mohajerani, and M. F. Hagan (2016), Many-molecule encapsulation by an icosahedral shell, eLife, 5, doi:10.7554/elife.14078. (published) [Maverick, TACC]
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A. Case (2014), Accurate small and wide angle x- ray scattering profiles from atomic models of proteins and nucleic acids, The Journal of Chemical Physics, 141(22), 22D508, doi:10.1063/1.4896220. (published) [Blacklight, Comet, Data Oasis, Data Supercell, FutureGrid, Globus Online, Gordon, HPSS, Keenland, Lonestar, Mason, Maverick, Ranch, Stampede, Trestles] 908. Nguyen, H. T., S. A. Pabit, L. Pollack, and D. A. Case (2016), Extracting water and ion distributions from solution x-ray scattering experiments, J. Chem. Phys., 144(21), 214105, doi:10.1063/1.4953037. (published) [Blacklight, Comet, Data Oasis, Data Supercell, FutureGrid, Globus Online, Gordon, HPSS, Keenland, Lonestar, Mason, Maverick, Ranch, Stampede, Trestles] 909. Panteva, M. T., G. M. Giambaşu, and D. M. York (2015), Force Field for Mg 2+ , Mn 2+ , Zn 2+ , and Cd 2+ Ions That Have Balanced Interactions with Nucleic Acids , J. Phys. Chem. B, 119(50), 15460–15470, doi:10.1021/acs.jpcb.5b10423. 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PY6 IPR 1 Page 138 170. TG-MCB130173 946. Botos, I., N. Majdalani, S. J. Mayclin, J. G. McCarthy, K. Lundquist, D. Wojtowicz, T. J. Barnard, J. C. Gumbart, and S. K. Buchanan (2016), Structural and Functional Characterization of the LPS Transporter LptDE from Gram- Negative Pathogens, Structure, 24(6), 965–976, doi:10.1016/j.str.2016.03.026. (published) 947. Deeng, J., K. Y. Chan, E. O. van der Sluis, O. Berninghausen, W. Han, J. Gumbart, K. Schulten, B. Beatrix, and R. Beckmann (2016), Dynamic Behavior of Trigger Factor on the Ribosome, Journal of Molecular Biology, 428(18), 3588–3602, doi:10.1016/j.jmb.2016.06.007. (published) 948. Deeng, J., K. Y. Chan, E. O. van der Sluis, O. Berninghausen, W. Han, J. Gumbart, K. Schulten, B. Beatrix, and R. Beckmann (2016), Dynamic Behavior of Trigger Factor on the Ribosome, Journal of Molecular Biology, 428(18), 3588–3602, doi:10.1016/j.jmb.2016.06.007. (published) 949. Gumbart, J. C., and C. Chipot (2016), Decrypting protein insertion through the translocon with free-energy calculations, Biochimica et Biophysica Acta (BBA) - Biomembranes, 1858(7), 1663–1671, doi:10.1016/j.bbamem.2016.02.017. (published) 950. Lee, C. T. et al. (2016), Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds, J. Chem. Inf. Model., 56(4), 721–733, doi:10.1021/acs.jcim.6b00022. (published) 951. Lundquist, K., C. Herndon, T. H. Harty, and J. C. Gumbart (2016), Accelerating the use of molecular modeling in the high school classroom with VMD Lite, Biochem. Mol. Biol. Educ., 44(2), 124–129, doi:10.1002/bmb.20940. (published) 952. Nguyen, L. T., J. C. Gumbart, and G. J. Jensen (2016), Coarse-Grained Molecular Dynamics Simulations of the Bacterial Cell Wall, Bacterial Cell Wall Homeostasis, 247–270, doi:10.1007/978-1-4939-3676-2_18. (published) 953. Pavlova, A., H. Hwang, K. Lundquist, C. Balusek, and J. C. Gumbart (2016), Living on the edge: Simulations of bacterial outer-membrane proteins, Biochimica et Biophysica Acta (BBA) - Biomembranes, 1858(7), 1753– 1759, doi:10.1016/j.bbamem.2016.01.020. (published) 171. TG-MCB140040 954. Kapoor, A., and A. Travesset (2015), Differential dynamics of RAS isoforms in GDP- and GTP-bound states, Proteins: Structure, Function, and Bioinformatics, 83(6), 1091–1106, doi:10.1002/prot.24805. (published) 172. TG-MCB140040, TG-MCB140071 955. Kapoor, A., and A. Travesset (2014), Mechanism of the Exchange Reaction in HRAS from Multiscale Modeling, edited by M. F. Olson, PLoS ONE, 9(10), e108846, doi:10.1371/journal.pone.0108846. (published) 173. TG-MCB140071 956. Kapoor, A., and A. Travesset (2013), Folding and stability of helical bundle proteins from coarse-grained models, Proteins: Structure, Function, and Bioinformatics, 81(7), 1200–1211, doi:10.1002/prot.24269. (published) 174. TG-MCB140084 957. Md Rejwan Ali and Mostafa Sadoqi, Potential Relativistic Dispersion in Material Medium. Results in Physics, (6): 178-179 [2016] 958. Rauf Latif, M Rejwan Ali, Risheng Ma, Martine David, Syed A Morshed, Dan P. Felsenfeld, Wingshan Lau, Mihaly Mezei & Terry F. Davies, New Small Molecule Agonists to the Thyrotropin Receptor, Thyroid. (25):51- 62 [2015]
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PY6 IPR 1 Page 144 1034. Editors: Jeanie Lau (UC-Riverside), Roland Kawakami (Ohio State) and Arthur Epstein (Ohio State). SBL. Discrete differential geometry and the properties of two-dimensional materials. Accepted on 6/27/2015. Invited Contribution to an upcoming issue on Advances in Graphene Science and Engineering. Synthetic Metals 210, 32 (2015). 1035. J.-A. Yan, M. A. Dela Cruz, SBL, and L. Yang. Strain-tunable topological quantum phase transition in buckled honeycomb lattices. Appl. Phys. Lett. 106, 183107 (2015). 1036. M. Mehboudi, K. Utt, H. Terrones, E. O. Harriss, A. A. Pacheco SanJuan, and SBL. Strain and the optoelectronic properties of non-planar phosphorene monolayers. Proc. Natl. Acad. Sci. (USA) 112, 5888 (2015). 1037. P. Rivero, C.M. Horvath, Z. Zhu, J. Guan, D. Tomanek, and SBL. Simulated scanning tunneling microscopy images of few-layer-phosphorus capped by hexagonal boron nitride and graphene monolayers. PRB 91, 115413 (2015). 1038. P. Rivero, V. M. Garcia-Suarez, D. Pereniguez, K. Utt, Y. Yang, L. Bellaiche, K. Park, J. Ferrer, and SBL. Systematic pseudopotentials from reference eigenvalue sets for DFT calculations: Pseudopotential files. Data in Brief 3, 21 (2015). 1039. P. Rivero, V. M. Garcia-Suarez, D. Pereniguez, K. Utt, Y. Yang, L. Bellaiche, K. Park, J. Ferrer, and SBL. Systematic pseudopotentials from reference eigenvalue sets for DFT calculations. Comp. Mat. Sci. 98, 372 (2015). 200. TG-PHY090003 1040. Berti, E. et al. (2015), Testing general relativity with present and future astrophysical observations, Classical and Quantum Gravity, 32(24), 243001, doi:10.1088/0264-9381/32/24/243001. (published) [SDSC, Stampede, TACC, Trestles] 1041. Berti, E., V. Cardoso, L. C. B. Crispino, L. Gualtieri, C. Herdeiro, and U. Sperhake (2016), Numerical relativity and high energy physics: Recent developments, Int. J. Mod. Phys. D, 25(09), 1641022, doi:10.1142/s0218271816410224. (published) [Comet, Data Oasis, PSC, SDSC, Stampede, TACC, Trestles] 1042. Cook, W. G., P. Figueras, M. Kunesch, U. Sperhake, and S. Tunyasuvunakool (2016), Dimensional reduction in numerical relativity: Modified Cartoon formalism and regularization, Int. J. Mod. Phys. D, 25(09), 1641013, doi:10.1142/s0218271816410133. (published) [Comet, Data Oasis, PSC, SDSC, Stampede, TACC, Trestles] 1043. Cook, W., Sperhake, U. 2016. Gravitational wave extraction in higher dimensional numerical relativity using the Weyl tensor. Classical and Quantum Gravity. (submitted) [Comet, Data Oasis, PSC, SDSC, Stampede, TACC, Trestles] 201. TG-PHY090031 1044. Schneider, B. I., K. Bartschat, and X. Guan (2016), Time Propagation of Partial Differential Equations Using the Short Iterative Lanczos Method and Finite-Element Discrete Variable Representation, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949565. (published) 202. TG-PHY120005 1045. Avara, M., McKinney, J., Reynolds, C. 2015. Thin Disks Gone MAD: Magnetically Arrested Accretion in the Thin Regime. American Astronomical Society Meeting Abstracts. 225. 225.06. (published) 1046. Avara, M., McKinney, J., Reynolds, C. 2015. Efficiency of Thin Magnetically-Arrested Disks Around Black Holes. ArXiv e-prints. (published) 1047. Avara, M., McKinney, J., Reynolds, C. 2016. Thin Disk Accretion in the Magnetically-Arrested State. American Astronomical Society Meeting Abstracts. 227. 203.02. (published) 1048. Bouman, K., Johnson, M., Zoran, D., Fish, V., Doeleman, S., et al. 2015. Computational Imaging for VLBI Image Reconstruction. ArXiv e-prints. (published) 1049. Bower, G. C. et al. (2015), THE PROPER MOTION OF THE GALACTIC CENTER PULSAR RELATIVE TO SAGITTARIUS A*, The Astrophysical Journal, 798(2), 120, doi:10.1088/0004-637x/798/2/120. (published) 1050. Bower, G. C. et al. (2016), ERRATUM: “THE PROPER MOTION OF THE GALACTIC CENTER PULSAR RELATIVE TO SAGITTARIUS A*” (2015, ApJ, 798, 120), The Astrophysical Journal, 821(2), 133, doi:10.3847/0004- 637x/821/2/133. (published) 1051. Bower, G., Demorest, P., Braatz, J., Broderick, A., Burke-Spolaor, S., et al. 2015. Next Generation Very Large Array Memo No. 9 Science Working Group 4: Time Domain, Fundamental Physics, and Cosmology. ArXiv e- prints. (published)
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Perepelitsky (2016), Low-energy physics of thet−Jmodel ind=∞using extremely correlated Fermi liquid theory: Cutoff second-order equations, Physical Review B, 94(4), doi:10.1103/physrevb.94.045138. (published) 1217. Shastry, B. S., E. Perepelitsky, and A. C. Hewson (2013), Extremely correlated Fermi liquid study of the U = ∞ Anderson impurity model , Physical Review B, 88(20), doi:10.1103/physrevb.88.205108. (published) 1218. Shi, J., Li, X., Cheng, H., Liu, Z., Zhao, L., et al. 2016. Graphene Reinforced Carbon Nanotube Networks for Wearable Strain Sensors. Advanced Functional Materials. (published) 1219. Silva, S. 2016. Maternal Exercise during Pregnancy Increases BDNF Levels and Cell Numbers in the Hippocampal Formation but Not in the Cerebral Cortex of Adult Rat Offspring. Plos one 11/1. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147200 (published) 1220. Solaimani, M., R. Gopalan, L. Khan, P. T. Brandt, and B. Thuraisingham (2016), Spark-Based Political Event Coding, 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService), doi:10.1109/bigdataservice.2016.30. (published)
PY6 IPR 1 Page 153 1221. Song, T., Cheng, H., Town, K., Park, H., Black, R., et al. 2014. Electrochemical Properties of Si-Ge Heterostructures as an Anode Material for Lithium Ion Batteries. Advanced Functional Materials 24: 1458-- 1464. (published) 1222. Spilker, J. S. et al. (2015), SUB-KILOPARSEC IMAGING OF COOL MOLECULAR GAS IN TWO STRONGLY LENSED DUSTY, STAR-FORMING GALAXIES, The Astrophysical Journal, 811(2), 124, doi:10.1088/0004- 637x/811/2/124. (published) 1223. Spilker, J. S. et al. (2014), THE REST-FRAME SUBMILLIMETER SPECTRUM OF HIGH-REDSHIFT, DUSTY, STAR-FORMING GALAXIES, The Astrophysical Journal, 785(2), 149, doi:10.1088/0004-637x/785/2/149. (published) 1224. Spilker, J. S. et al. (2016), ALMA IMAGING AND GRAVITATIONAL LENS MODELS OF SOUTH POLE TELESCOPE—SELECTED DUSTY, STAR-FORMING GALAXIES AT HIGH REDSHIFTS, The Astrophysical Journal, 826(2), 112, doi:10.3847/0004-637x/826/2/112. (published) 1225. Staton, M. et al. (2015), Preliminary Genomic Characterization of Ten Hardwood Tree Species from Multiplexed Low Coverage Whole Genome Sequencing, edited by F. A. Aravanopoulos, PLoS ONE, 10(12), e0145031, doi:10.1371/journal.pone.0145031. (published) 1226. Staton, M., T. Zhebentyayeva, B. Olukolu, G. C. Fang, D. Nelson, J. E. Carlson, and A. G. Abbott (2015), Substantial genome synteny preservation among woody angiosperm species: comparative genomics of Chinese chestnut (Castanea mollissima) and plant reference genomes, BMC Genomics, 16(1), doi:10.1186/s12864-015-1942-1. (published) 1227. Strandet, M. L. et al. (2016), THE REDSHIFT DISTRIBUTION OF DUSTY STAR-FORMING GALAXIES FROM THE SPT SURVEY, The Astrophysical Journal, 822(2), 80, doi:10.3847/0004-637x/822/2/80. (published) 1228. Su, Y., Li, R., Cheng, H., Ying, M., Bonifas, A., et al. 2013. Mechanics of finger-tip electronics. Journal of applied physics 114: 164511. (published) 1229. Tahmasbi, R. 2014. Modeling and forecasting the urban volume using stochastic differential equations. IEEE Transactions on Intelligent Transportation Systems 15(1): 250-259. (published) 1230. Tahmasbi, R., M. C. Keller, . 2016. GeneEvolve: a fast and memory efficient forward-time simulator of realistic whole-genome sequence and SNP data. Bioinformatics Under review. (published) 1231. Tahmasbi, R., Nasrabadi, E., Hashemi, S. 2013. The value of information in stochastic maximum flow problems. Computers & Operations Research 40: 1744--1751. (published) 1232. Talipov, M. R., Q. K. Timerghazin, R. L. Safiullin, and S. L. Khursan (2013), No Longer a Complex, Not Yet a Molecule: A Challenging Case of Nitrosyl O -Hydroxide, HOON , The Journal of Physical Chemistry A, 117(3), 679–685, doi:10.1021/jp3110858. (published) [Comet, Data Oasis, SDSC] 1233. Tlach, B. C., A. L. Tomlinson, K. D. Morgan, C. R. Collins, M. D. Zenner, and M. Jeffries-EL (2014), Effect of Extended Conjugation on the Optoelectronic Properties of Benzo[1,2-d:4,5-d′]bisoxazole Polymers, Australian Journal of Chemistry, 67(5), 711, doi:10.1071/ch13528. (published) 1234. Tlach, B. C., A. L. Tomlinson, A. G. Ryno, D. D. Knoble, D. L. Drochner, K. J. Krager, and M. Jeffries-EL (2013), Influence of Conjugation Axis on the Optical and Electronic Properties of Aryl-Substituted Benzobisoxazoles, J. Org. Chem., 78(13), 6570–6581, doi:10.1021/jo4007927. (published) 1235. Tomlinson, A., Chavez III, R., Jeffries-EL, M., Cai, M., Tlach, B., et al. 2016. Benzobisoxazole cruciforms: A tunable, cross-conjugated platform for the generation of deep blue OLED materials. Journal of Materials Chemistry C 4: 3765-3773. (published) 1236. Travesset, A. (2015), Binary nanoparticle superlattices of soft-particle systems, Proc Natl Acad Sci USA, 112(31), 9563–9567, doi:10.1073/pnas.1504677112. (published) 1237. Travesset, A. (2016), Topological structure prediction in binary nanoparticle superlattices, Soft Matter, doi:10.1039/c6sm00713a. (published) 1238. K Trived, M. (2014), Atomic, Crystalline and Powder Characteristics of Treated Zirconia and Silica Powders, Journal of Material Science & Engineering, 03(03), doi:10.4172/2169-0022.1000144. (published) [Science Gateways] 1239. Kumar Trivedi, M. (2015), Spectroscopic Characterization of Chloramphenicol and Tetracycline: An Impact of Biofield Treatment, Pharmaceutica Analytica Acta, 06(07), doi:10.4172/2153-2435.1000395. (published) [Science Gateways] 1240. Trivedi, M. K. (2015), An Impact of Biofield Treatment on Spectroscopic Characterization of Pharmaceutical Compounds, Mod Chem Appl, 03(03), doi:10.4172/2329-6798.1000159. (published) [Science Gateways] 1241. Kumar Trivedi, M. (2015), An Impact of Biofield Treatment: Antimycobacterial Susceptibility Potential Using BACTEC 460/MGIT-TB System, Mycobact Dis, 05(04), doi:10.4172/2161-1068.1000189. (published) [Science Gateways]
PY6 IPR 1 Page 154 1242. Anon (2015), Structural and Physical Properties of Biofield Treated Thymol and Menthol, J Mol Pharm Org Process Res, 03(02), doi:10.4172/2329-9053.1000127. (published) [Science Gateways] 1243. Trivedi, M. K., S. P. Harish Shettigar, and K. B. Snehasis Jana (2015), Effect of Biofield Treatment on Phenotypic and Genotypic Characteristic of Provindencia rettgeri, Mol Biol, 04(03), doi:10.4172/2168- 9547.1000129. (published) [Science Gateways] 1244. Trivedi, M. K., and S. P. Harish Shettigar (2015), Evaluation of Phenotyping and Genotyping Characteristic of Shigella sonnei after Biofield Treatment, Journal of Biotechnology & Biomaterials, 05(03), doi:10.4172/2155- 952x.1000196. (published) [Science Gateways] 1245. Kumar Trivedi, M. (2015), Spectroscopic Characterization of Biofield Treated Metronidazole and Tinidazole, Med chem, 5(7), doi:10.4172/2161-0444.1000283. (published) [Science Gateways] 1246. Kumar Trivedi, M. (2015), Biofield Treatment: An Alternative Approach to Combat Multidrug-Resistant Susceptibility Pattern of Raoultella ornithinolytica, Alternative & Integrative Medicine, 04(03), doi:10.4172/2327-5162.1000193. (published) [Science Gateways] 1247. MK, T. (2015), Effect of Biofield Treatment on Spectral Properties of Paracetamol and Piroxicam, Chemical Sciences Journal, 6(3), doi:10.4172/2150-3494.100098. (published) [Science Gateways] 1248. Patil S, T. M., and S. H. Bairwa K (2015), Evaluation of Phenotyping and Genotyping Characterization of Serratia marcescens after Biofield Treatment, J Mol Genet Med, 09(03), doi:10.4172/1747-0862.1000179. (published) [Science Gateways] 1249. Kumar Trivedi, M. (2015), An Evaluation of Biofield Treatment on Susceptibility Pattern of Multidrug Resistant Stenotrophomonas maltophilia: An Emerging Global Opportunistic Pathogen, Clinical Microbiology: Open Access, 04(04), doi:10.4172/2327-5073.1000211. (published) [Science Gateways] 1250. Kumar Trivedi, M. (2015), Phenotypic and Biotypic Characterization of Klebsiella oxytoca: An Impact of Biofield Treatment, Journal of Microbial & Biochemical Technology, 07(04), doi:10.4172/1948- 5948.1000205. (published) [Science Gateways] 1251. Jana, S. (2015), Thermal andPhysical Properties of Biofield Treated Bile Salt and Proteose Peptone, Journal of Analytical & Bioanalytical Techniques, 6(4), doi:10.4172/2155-9872.1000256. (published) [Science Gateways] 1252. Trivedi, M. K., and S. P. Harish Shettigar (2015), In vitro Evaluation of Biofield Treatment on Enterobacter cloacae: Impact on Antimicrobial Susceptibility and Biotype, J Bacteriol Parasitol, 06(05), doi:10.4172/2155- 9597.1000241. (published) [Science Gateways] 1253. Trivedi, M. K., and S. P. Harish Shettigar (2015), The Potential Impact of Biofield Treatment on Human Brain Tumor Cells: A Time-Lapse Video Microscopy, Journal of Integrative Oncology, 04(03), doi:10.4172/2329- 6771.1000141. (published) [Science Gateways] 1254. Trivedi, M. K., S. P. Harish Shettigar, and M. G. Snehasis Jana (2015), In Vitro Evaluation of Biofield Treatment on Cancer Biomarkers Involved in Endometrial and Prostate Cancer Cell Lines, Journal of Cancer Science & Therapy, 07(08), doi:10.4172/1948-5956.1000358. (published) [Science Gateways] 1255. Kumar Trivedi, M. (2015), Mass Spectrometry Analysis of Isotopic Abundance of 13C, 2H, or 15N in Biofield Energy Treated Aminopyridine Derivatives, American Journal of Physical Chemistry, 4(6), 65, doi:10.11648/j.ajpc.20150406.14. (published) [Science Gateways] 1256. Trivedi, M. K., A. B. Dahryn Trivedi, and H. S. Khemraj Bairwa (2015), Fourier Transform Infrared and Ultraviolet-Visible Spectroscopic Characterization of Biofield Treated Salicylic Acid and Sparfloxacin, Nat Prod Chem Res, 03(05), doi:10.4172/2329-6836.1000186. (published) [Science Gateways] 1257. Trivedi, M., Branton, A., Trivedi, D. 2015. Antibiogram Typing and Biochemical Characterization of Klebsiella pneumoniae after Biofield Treatment. Journal of Tropical Diseases 3/4. http://trivediscience.com/publications/microbiology-publications/antibiogram-typing-and-biochemical- characterization-of-klebsiella-pneumoniae-after-biofield-treatment/ DOI:10.4173/2329891X.1000173 (Invalid?). (published) [Science Gateways] 1258. Branton A, T. M., T. D. Nayak G, and B. K. Jana S (2015), Physical, Thermal and Spectroscopical Characterization of Biofield Treated Triphenylmethane: An Impact of Biofield Treatment, J Chromatograph Separat Techniq, 06(06), doi:10.4172/2157-7064.1000292. (published) [Science Gateways] 1259. Kumar Trivedi, M. (2015), Physicochemical and Spectroscopic Characterization of Yeast Extract Powder After the Biofield Energy Treatment, AJLS, 3(6), 387, doi:10.11648/j.ajls.20150306.12. (published) [Science Gateways] 1260. Kumar Trivedi, M. (2015), Characterization of Atomic and Physical Properties of Biofield Energy Treated Manganese Sulfide Powder, American Journal of Physics and Applications, 3(6), 215, doi:10.11648/j.ajpa.20150306.15. (published) [Science Gateways]
PY6 IPR 1 Page 155 1261. Kumar Trivedi, M. (2015), Comparative Physicochemical Evaluation of Biofield Treated Phosphate Buffer Saline and Hanks Balanced Salt Medium, AJBIO, 3(6), 267, doi:10.11648/j.ajbio.20150306.20. (published) [Science Gateways] 1262. Kumar Trivedi, M. (2016), Physicochemical Characterization of Biofield Energy Treated Hi VegTM Acid Hydrolysate, IJNFS, 5(1), 1, doi:10.11648/j.ijnfs.20160501.11. (published) [Science Gateways] 1263. Kumar Trivedi, M., and A. Branton (2015), Isotopic Abundance Analysis of Biofield Treated Benzene, Toluene and p-Xylene Using Gas Chromatography-Mass Spectrometry (GC-MS), Mass Spectrometry & Purification Techniques, 01(01), doi:10.4172/2469-9861.1000102. (published) [Science Gateways] 1264. Kumar Trivedi, M., and A. Branton (2015), Physicochemical and Spectroscopic Characterization of Biofield Energy Treated p-Anisidine, Pharm Anal Chem, 01(01), doi:10.4172/2471-2698.1000102. (published) [Science Gateways] 1265. Kumar Trivedi, M. (2015), Quantitative Determination of Isotopic Abundance Ratio of 13C, 2H, and 18O in Biofield Energy Treated Ortho and Meta Toluic Acid Isomers, AJAC, 3(6), 217, doi:10.11648/j.ajac.20150306.17. (published) [Science Gateways] 1266. Kumar Trivedi, M. (2015), Characterization of Physical, Thermal and Spectral Properties of Biofield Treated 2-Aminopyridine, SJAC, 3(6), 127, doi:10.11648/j.sjac.20150306.18. (published) [Science Gateways] 1267. Jana, S. (2015), Spectroscopic Characterization of Disulfiram and Nicotinic Acid after Biofield Treatment, Journal of Analytical & Bioanalytical Techniques, 6(5), doi:10.4172/2155-9872.1000265. (published) [Science Gateways] 1268. Kumar Trivedi, M. (2015), Biochemical Differentiation and Molecular Characterization of Biofield Treated Vibrio parahaemolyticus, American Journal of Clinical and Experimental Medicine, 3(5), 260, doi:10.11648/j.ajcem.20150305.21. (published) [Science Gateways] 1269. Kumar Trivedi, M. (2015), Effect of Biofield Treatment on Physical, Thermal, and Spectral Properties of SFRE 199-1 Mammalian Cell Culture Medium, AB, 3(6), 77, doi:10.11648/j.ab.20150306.13. (published) [Science Gateways] 1270. Kumar Trivedi, M. (2015), Agronomic Characteristics, Growth Analysis, and Yield Response of Biofield Treated Mustard, Cowpea, Horse Gram, and Groundnuts, International Journal of Genetics and Genomics, 3(6), 74, doi:10.11648/j.ijgg.20150306.13. (published) [Science Gateways] 1271. Kumar Trivedi, M. (2015), Evaluation of Biofield Energy Treatment on Physical and Thermal Characteristics of Selenium Powder, Journal of Food and Nutrition Sciences, 3(6), 223, doi:10.11648/j.jfns.20150306.14. (published) [Science Gateways] 1272. Kumar Trivedi, M. (2015), Analysis of Genetic Diversity Using Simple Sequence Repeat (SSR) Markers and Growth Regulator Response in Biofield Treated Cotton (Gossypium hirsutum L.), American Journal of Agriculture and Forestry, 3(5), 216, doi:10.11648/j.ajaf.20150305.17. (published) [Science Gateways] 1273. Anon (2015), Antimicrobial Susceptibility, Biochemical Characterization and Molecular Typing of Biofield Treated Klebsiella pneumoniae, J Health Med Informat, 06(05), doi:10.4172/2157-7420.1000206. (published) [Science Gateways] 1274. Kumar Trivedi, M., and A. Branton (2015), Assessment of Antibiogram of Multidrug-Resistant Isolates of Enterobacter aerogenes after Biofield Energy Treatment, J Pharma Care Health Sys, 02(05), doi:10.4172/2376-0419.1000145. (published) [Science Gateways] 1275. Kumar Trivedi, M. (2015), Antibiogram Typing of Biofield Treated Multidrug Resistant Strains of Staphylococcus Species, AJLS, 3(5), 369, doi:10.11648/j.ajls.20150305.16. (published) [Science Gateways] 1276. Kumar Trivedi, M., and A. Branton (2015), Evaluation of Antibiogram, Genotype and Phylogenetic Analysis of Biofield Treated Nocardia otitidis, Biological Systems: Open Access, 04(02), doi:10.4172/2329- 6577.1000143. (published) [Science Gateways] 1277. Kumar Trivedi, M. (2015), Physicochemical and Spectroscopic Properties of Biofield Energy Treated Protose, AJBLS, 3(6), 104, doi:10.11648/j.ajbls.20150306.11. (published) 1278. Rama MT, M. K., and A. B. Dahryn T (2015), Bio-field Treatment: A Potential Strategy for Modification of Physical and Thermal Properties of Gluten Hydrolysate and Ipomoea Macroelements, Journal of Nutrition & Food Sciences, 05(05), doi:10.4172/2155-9600.1000414. (published) [Science Gateways] 1279. Kumar Trivedi, M. (2015), Phenotyping and Genotyping Characterization of Proteus vulgaris After Biofield Treatment, International Journal of Genetics and Genomics, 3(6), 66, doi:10.11648/j.ijgg.20150306.12. (published)
PY6 IPR 1 Page 156 1280. Trivedi, M. K., A. B. Dahryn Trivedi, and G. N. Harish Shettigar (2015), Effect of Biofield Energy Treatment on Streptococcus group B: A Postpartum Pathogen, Journal of Microbial & Biochemical Technology, 07(05), doi:10.4172/1948-5948.1000223. (published) [Science Gateways] 1281. Kumar Trivedi, M. (2015), Evaluation of Plant Growth Regulator, Immunity and DNA Fingerprinting of Biofield Energy Treated Mustard Seeds (Brassica juncea), Agriculture, Forestry and Fisheries, 4(6), 269, doi:10.11648/j.aff.20150406.16. (published) [Science Gateways] 1282. Kumar Trivedi, M. (2015), Antibiogram Pattern of Shigella flexneri: Effect of Bio Field Treatment, Journal Air and Water borne diseases, 04(02), doi:10.4172/2167-7719.1000122. (published) [Science Gateways] 1283. Kumar Trivedi, M. (2015), Physicochemical Characterization of Biofield Energy Treated Calcium Carbonate Powder, AJHR, 3(6), 368, doi:10.11648/j.ajhr.20150306.19. (published) [Science Gateways] 1284. Kumar Trivedi, M., and A. Branton (2015), Bacterial Identification Using 16S rDNA Gene Sequencing and Antibiogram Analysis on Biofield Treated Pseudomonas fluorescens, Clinical & Medical Biochemistry: Open Access, 01(01), doi:10.4172/2471-2663.1000101. (published) [Science Gateways] 1285. Trivedi, M. K., A. B. Dahryn Trivedi, and G. N. Gunin Saikia (2015), Physical and Structural Characterization of Biofield Treated Imidazole Derivatives, Nat Prod Chem Res, 03(05), doi:10.4172/2329-6836.1000187. (published) [Science Gateways] 1286. Trivedi, M. K., and A. Branton (2015), Antibiogram and Genotypic Analysis using 16S rDNA after Biofield Treatment on Morganella morganii, Adv Tech Biol Med, 03(03), doi:10.4172/2379-1764.1000137. (published) [Science Gateways] 1287. Trivedi, M. K., and R. M. Tallapragada (2015), Characterization of Physical, Spectral and Thermal Properties of Biofield Treated 1,2,4-Triazole, J Mol Pharm Org Process Res, 03(02), doi:10.4172/2329-9053.1000128. (published) [Science Gateways] 1288. Dahryn Trivedi, A. B. (2015), Antimicrobial Susceptibility Pattern, Biochemical Characteristics and Biotyping of Salmonella paratyphi A: An Impact of Biofield Treatment, Clinical Microbiology: Open Access, 04(04), doi:10.4172/2327-5073.1000215. (published) [Science Gateways] 1289. Kumar Trivedi, M. (2015), Antibiogram, Biochemical Reactions and Genotyping Characterization of Biofield Treated Staphylococcus aureus, AJBIO, 3(6), 212, doi:10.11648/j.ajbio.20150306.13. (published) [Science Gateways] 1290. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2015. Phenotyping and 16S rDNA Analysis after Biofield Treatment on Citrobacter braakii: A Urinary Pathogen. Journal of Clinical & Medical Genomics 3/1. http://trivediscience.com/publications/microbiology-publications/phenotyping-and-16s-rdna-analysis- after-biofield-treatment-on-citrobacter-braakii-a-urinary-pathogen/ DOI:10.4172/jcmg.1000129 (Invalid?). (published) [Science Gateways] 1291. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2015. Impact of Biofield Treatment on Spectroscopic and Physicochemical Properties of p-Nitroaniline. Insights in Analytical Electrochemistry 1/1. http://trivediscience.com/publications/pharmaceuticals-publications/impact-of-biofield-treatment-on- spectroscopic-and-physicochemical-properties-of-p-nitroaniline/ DOI:10.4172/2470-9867.100002 (Invalid?). (published) [Science Gateways] 1292. Kumar Trivedi, M. (2015), Physicochemical and Spectroscopic Characterization of Biofield Energy Treated Gerbera Multiplication Medium, Plant, 3(6), 57, doi:10.11648/j.plant.20150306.11. (published) [Science Gateways] 1293. Trivedi, M. K. (2015), Physical, Thermal and Spectroscopic Characterization of m-Toluic Acid: an Impact of Biofield Treatment, Biochem Pharmacol (Los Angel), 04(04), doi:10.4172/2167-0501.1000178. (published) [Science Gateways] 1294. S et al, J. (2015), Characterization of Antimicrobial Susceptibility Profile of Biofield Treated Multidrug- resistant Klebsiella oxytoca, Applied Microbiology: open access, 02(02), doi:10.4172/2471-9315.1000101. (published) [Science Gateways] 1295. Kumar Trivedi, M. (2015), Physicochemical and Spectroscopic Characteristics of Biofield Treated p- Chlorobenzophenone, American Journal of Physical Chemistry, 4(6), 48, doi:10.11648/j.ajpc.20150406.12. (published) [Science Gateways] 1296. MK, T., T. RM, and B. A (2015), Evaluation of Atomic, Physical and Thermal Properties of Tellurium Powder: Impact of Biofield Energy Treatment, J Elec Electron Syst, 04(03), doi:10.4172/2332-0796.1000162. (published) [Science Gateways] 1297. Trivedi, M. K., and A. B. Dahryn Trivedi (2015), Spectroscopic Characterization of Disodium Hydrogen Orthophosphate and Sodium Nitrate after Biofield Treatment, Journal of Chromatography & Separation Techniques, 06(05), doi:10.4172/2157-7064.1000282. (published) [Science Gateways]
PY6 IPR 1 Page 157 1298. Kumar Trivedi, M., A. Branton, D. Trivedi, G. Nayak, and R. Singh (2015), Characterization of Physical, Thermal and Spectroscopic Properties of Biofield Energy Treated p-Phenylenediamine and p-Toluidine, Journal of Environmental & Analytical Toxicology, 05(06), doi:10.4172/2161-0525.1000329. (published) [Science Gateways] 1299. Kumar Trivedi, M., A. Branton, and D. Trivedi (2015), Physical, Thermal and Spectroscopic Characterization of Biofield Treated p-Chloro-m-cresoln, Journal of Chemical Engineering & Process Technology, 06(05), doi:10.4172/2157-7048.1000249. (published) [Science Gateways] 1300. Kumar Trivedi, M. (2015), The Potential Impact of Biofield Energy Treatment on the Physical and Thermal Properties of Silver Oxide Powder, IJBSE, 3(5), 62, doi:10.11648/j.ijbse.20150305.11. (published) [Science Gateways] 1301. Kumar Trivedi, M. (2015), Fourier Transform Infrared and Ultraviolet-Visible Spectroscopic Characterization of Ammonium Acetate and Ammonium Chloride: An Impact of Biofield Treatment, Mod Chem Appl, 03(03), doi:10.4172/2329-6798.1000163. (published) [Science Gateways] 1302. Kumar Trivedi, M., and R. M. Tallapragada (2015), Potential Impact of BioField Treatment on Atomic and Physical Characteristics of Magnesium, Vitam Miner, 04(03), doi:10.4172/2376-1318.1000129. (published) [Science Gateways] 1303. Kumar T, M. (2015), Biofield Treatment: A Potential Strategy for Modification of Physical and Thermal Properties of Indole, J Environ Anal Chem, 02(04), doi:10.4172/2380-2391.1000152. (published) [Science Gateways] 1304. Kumar Trivedi, M. (2015), Studies on Physicochemical Properties of Biofield Treated 2,4-Dichlorophenol, AJEP, 4(6), 292, doi:10.11648/j.ajep.20150406.15. (published) [Science Gateways] 1305. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2015. Physical, Spectroscopic and Thermal Characterization of Biofield treated Myristic acid. Fundamentals of Renewable Energy and Applications 5/5. http://trivediscience.com/publications/pharmaceuticals-publications/physical-spectroscopic-and-thermal- characterization-of-biofield-treated-myristic-acid/ DOI:10.4172/20904541.1000180 (Invalid?). (published) [Science Gateways] 1306. Anon (2015), Physical, Thermal and Spectroscopic Studies on Biofield Treated p-Dichlorobenzene, Biochem Anal Biochem, 04(04), doi:10.4172/2161-1009.1000204. (published) [Science Gateways] 1307. Kumar Trivedi, M. (2015), Physicochemical Evaluation of Biofield Treated Peptone and Malmgren Modified Terrestrial Orchid Medium, BIO, 3(6), 169, doi:10.11648/j.bio.20150306.15. (published) [Science Gateways] 1308. Kumar Trivedi, M. (2015), Physical, Thermal, and Spectroscopic Characterization of Biofield Energy Treated Murashige and Skoog Plant Cell Culture Media, CB, 3(4), 50, doi:10.11648/j.cb.20150304.11. (published) [Science Gateways] 1309. Kumar Trivedi, M. (2015), Physicochemical and Spectral Characterization of Biofield Energy Treated 4- Methylbenzoic Acid, AJCHE, 3(6), 99, doi:10.11648/j.ajche.20150306.14. (published) [Science Gateways] 1310. Kumar Trivedi, M. (2015), Effect of Biofield Energy Treatment on Physical and Structural Properties of Calcium Carbide and Praseodymium Oxide, International Journal of Materials Science and Applications, 4(6), 390, doi:10.11648/j.ijmsa.20150406.14. (published) [Science Gateways] 1311. Kumar Trivedi, M. (2016), Determination of Isotopic Abundance of 2H, 13C, 18O, and 37Cl in Biofield Energy Treated Dichlorophenol Isomers, SJAC, 4(1), 1, doi:10.11648/j.sjac.20160401.11. (published) [Science Gateways] 1312. Kumar Trivedi, M. (2015), Evaluation of Plant Growth, Yield and Yield Attributes of Biofield Energy Treated Mustard (Brassica juncea) and Chick Pea (Cicer arietinum) Seeds, Agriculture, Forestry and Fisheries, 4(6), 291, doi:10.11648/j.aff.20150406.19. (published) [Science Gateways] 1313. Trivedi, M. K., A. B. Dahryn Trivedi, G. N. Khemraj Bairwa, and S. Jana (2015), Physical, Thermal, and Spectroscopic Characterization of Biofield Energy Treated Methyl-2-Naphthyl Ether, J Environ Anal Chem, 02(05), doi:10.4172/2380-2391.1000162. (published) [Science Gateways] 1314. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2015. Characterization of Physical and Thermal Properties of Biofield Treated Neopentyl Glycol. Pharmaceutical Analytical Chemistry: Open Access 1/1. http://trivediscience.com/publications/pharmaceuticals-publications/characterization-of-physical-and- thermal-properties-of-biofield-treated-neopentyl-glycol/ DOI:10.1012/2471-2698.1000101 (Invalid?). (published) [Science Gateways] 1315. Kumar Trivedi, M. (2015), Physical, Thermal and Spectral Properties of Biofield Treated 3- Nitroacetophenone, SJAC, 3(6), 71, doi:10.11648/j.sjac.20150306.11. (published) [Science Gateways] 1316. Trivedi, M. K., R. M. Tallapragada, and A. B. Dahryn Trivedi (2015), Physical, Thermal and Spectral Properties of Biofield Treated 1,2,3-Trimethoxybenzene, J Develop Drugs, 04(04), doi:10.4172/2329-6631.1000136. (published) [Science Gateways]
PY6 IPR 1 Page 158 1317. Kumar Trivedi, M. (2015), Physical, Thermal and Spectroscopic Studies of Biofield Treated p- Chlorobenzonitrile, SJC, 3(6), 84, doi:10.11648/j.sjc.20150306.11. (published) [Science Gateways] 1318. Trivedi, M. K., and R. M. Tallapragada (2015), Characterization of Physical, Thermal and Spectral Properties of Biofield Treated 2, 6-Diaminopyridine, J Dev Drugs, 04(03), doi:10.4172/2329-6631.1000133. (published) [Science Gateways] 1319. Kumar Trivedi, M., R. M. Tallapragada, A. Branton, and D. Trivedi (2015), Physical, Thermal and Spectral Properties of Biofield Energy Treated 2,4-Dihydroxybenzophenone, Clin Pharmacol Biopharm, 04(04), doi:10.4172/2167-065x.1000145. (published) [Science Gateways] 1320. Kumar Trivedi, M., and A. Branton (2015), Investigation of Biofield Treatment on Antimicrobial Susceptibility, Biochemical Reaction Pattern and Biotyping of Enteropathogenic Multidrug-Resistant Escherichia coli Isolates, Gen Med (Los Angel), s2, doi:10.4172/2327-5146.1000s2-002. (published) [Science Gateways] 1321. MK, T., and B. A (2015), Investigation of Isotopic Abundance Ratio of Biofield Treated Phenol Derivatives Using Gas Chromatography-Mass Spectrometry, J Chromatograph Separat Techniq, s6, doi:10.4172/2157- 7064.s6-003. (published) [Science Gateways] 1322. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2015. Evaluation of Physical, Thermal and Spectroscopic Properties of Biofield Treated p-Hydroxyacetophenone. Natural Products Chemistry & Research 3/5. http://trivediscience.com/publications/pharmaceuticals-publications/evaluation-of-physical-thermal-and- spectroscopic-properties-of-biofield-treated-p-hydroxyacetophenone/ DOI:10.4172/2329- 6836.1000190 (Invalid?). (published) [Science Gateways] 1323. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Isotopic Abundance Ratio Analysis of Biofield Energy Treated Indole Using Gas Chromatography-Mass Spectrometry. Science Journal of Chemistry 4/4: 41-48. http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=125&doi=10.11648/j.sjc.20160404.1 1 DOI:10.11648/j.sjc.20160404.11 (Invalid?). (published) [Science Gateways] 1324. Kumar Trivedi, M. (2016), Evaluation of Isotopic Abundance Ratio in Biofield Energy Treated Nitrophenol Derivatives Using Gas Chromatography-Mass Spectrometry, AJCHE, 4(3), 68, doi:10.11648/j.ajche.20160403.11. (published) [Science Gateways] 1325. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Determination of Isotopic Abundance Ratio of Biofield Energy Treated 1,4-Dichlorobenzene Using Gas Chromatography-Mass Spectrometry (GC-MS). Modern Chemistry 4/3: 30-37. http://article.sciencepublishinggroup.com/html/10.11648.j.mc.20160403.11.html DOI:10.11648/j.mc.20160403.11 (Invalid?). (published) [Science Gateways] 1326. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Mass Spectrometric Analysis of Isotopic Abundance Ratio in Biofield Energy Treated Thymol. Frontiers in Applied Chemistry 1/1: 1-8. http://article.sciencepublishinggroup.com/html/10.11648.j.fac.20160101.11.html DOI:10.11648/j.fac.20160101.11 (Invalid?). (published) [Science Gateways] 1327. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Gas Chromatography-Mass Spectrometric Analysis of Isotopic Abundance of 13C, 2H, and 18O in Biofield Energy Treated p-tertiary Butylphenol (PTBP). American Journal of Chemical Engineering 4/4: 78-86. http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=224&doi=10.11648/j.ajche.2016040 4.11 DOI:10.11648/j.ajche.20160404.11 (Invalid?). (published) [Science Gateways] 1328. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Gas Chromatography-Mass Spectrometry Based Isotopic Abundance Ratio Analysis of Biofield Energy Treated Methyl-2-napthylether (Nerolin). American Journal of Physical Chemistry 5/4: 80-86. http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=128&doi=10.11648/j.ajpc.20160504. 11 DOI:10.11648/j.ajpc.20160504.11 (Invalid?). (published) [Science Gateways] 1329. Kumar T, M., and A. B (2016), Evaluation of the Isotopic Abundance Ratio in Biofield Energy Treated Resorcinol Using Gas Chromatography-Mass Spectrometry Technique, Pharmaceutica Analytica Acta, 07(05), doi:10.4172/2153-2435.1000481. (published) [Science Gateways] 1330. Trivedi, M., Branton, A., Trivedi, D., Nayak, G. 2016. Determination of Isotopic Abundance of 13C/12C or 2H/1H and 18O/16O in Biofield Energy Treated 1-Chloro-3-Nitrobenzene (3-CNB) Using Gas Chromatography-Mass Spectrometry. Science Journal of Analytical Chemistry 4/4: 42-51. http://article.sciencepublishinggroup.com/html/10.11648.j.sjac.20160404.11.html DOI:10.11648/j.sjac.20160404.11 (Invalid?). (published) [Science Gateways] 1331. Kumar Trivedi, M. (2016), Isotopic Abundance Ratio Analysis of 1,2,3-Trimethoxybenzene (TMB) After Biofield Energy Treatment (The Trivedi Effect®) Using Gas Chromatography-Mass Spectrometry, AJAC, 4(4), 132, doi:10.11648/j.ajac.20160404.13. (published) [Science Gateways]
PY6 IPR 1 Page 159 1332. Nayak G, T. M. (2015), Evaluation of Biofield Treatment on Physical, Atomic and Structural Characteristics of Manganese (II, III) Oxide, Journal of Material Science & Engineering, 04(04), doi:10.4172/2169- 0022.1000177. (published) [Science Gateways] 1333. Trivedi, M. K. (2015), Influence of Biofield Treatment on Physicochemical Properties of Hydroxyethyl Cellulose and Hydroxypropyl Cellulose, J Mol Pharm Org Process Res, 03(02), doi:10.4172/2329- 9053.1000126. (published) [Science Gateways] 1334. Trivedi M, J. S. (2015), Characterization of Physical and Structural Properties of Brass Powder After Biofield Treatment, Journal of Powder Metallurgy & Mining, 04(01), doi:10.4172/2168-9806.1000134. (published) [Science Gateways] 1335. MK, T., and N. G (2015), Evaluation of the Impact of Biofield Treatment on Physical and Thermal Properties of Casein Enzyme Hydrolysate and Casein Yeas t Peptone, Clinical Pharmacology & Biopharmaceutics, 4(2), doi:10.4172/2167-065x.1000138. (published) [Science Gateways] 1336. Nayak G, T. M. (2015), Effect of Biofield Treatment on Structural and Morphological Properties of Silicon Carbide, Journal of Powder Metallurgy & Mining, 04(01), doi:10.4172/2168-9806.1000132. (published) [Science Gateways] 1337. Kumar Trivedi, M., and G. Nayak (2015), The Potential Impact of Biofield Treatment on Physical, Structural and Mechanical Properties of Stainless Steel Powder, Journal of Applied Mechanical Engineering, 04(04), doi:10.4172/2168-9873.1000173. (published) [Science Gateways] 1338. Omprakash L, S. J., M. K. Gopal N, and S. P. Mohan RT (2015), Evaluation of Bio-field Treatment on Physical and Structural Properties of Bronze Powder, Adv Automob Eng, 04(01), doi:10.4172/2167-7670.1000119. (published) [Science Gateways] 1339. Gopal N, M. K. (2015), Bio-field Treatment: An Effective Strategy to Improve the Quality of Beef Extract and Meat Infusion Powder, Journal of Nutrition & Food Sciences, 05(04), doi:10.4172/2155-9600.1000389. (published) [Science Gateways] 1340. Trivedi, M. K. (2015), Impact of Biofield Treatment on Physical, Structural and Spectral Properties of Antimony Sulfide, Industrial Engineering & Management, 04(03), doi:10.4172/2169-0316.1000165. (published) [Science Gateways] 1341. Kumar Trivedi, M. (2015), Studies of the Atomic and Crystalline Characteristics of Ceramic Oxide Nano Powders after Bio field Treatment, Industrial Engineering & Management, 04(03), doi:10.4172/2169- 0316.1000161. (published) [Science Gateways] 1342. Anon (2015), An Evaluation of Biofield Treatment on Thermal, Physical and Structural Properties of Cadmium Powder, Journal of Thermodynamics & Catalysis, 06(02), doi:10.4172/2157-7544.1000147. (published) [Science Gateways] 1343. Nayak G, T. M. (2015), Impact of Biofield Treatment on Atomic and Structural Characteristics of Barium Titanate Powder, Industrial Engineering & Management, 04(03), doi:10.4172/2169-0316.1000166. (published) [Science Gateways] 1344. Patil S, T. M. (2015), Influence of Biofield Treatment on Physical, Structural and Spectral Properties of Boron Nitride, Journal of Material Science & Engineering, 04(04), doi:10.4172/2169-0022.1000181. (published) [Science Gateways] 1345. Kumar Trivedi, M. (2015), Characterization of Physicochemical and Spectroscopic Properties of Biofield Energy Treated Bio Peptone, Advances in Bioscience and Bioengineering, 3(6), 59, doi:10.11648/j.abb.20150306.12. (published) [Science Gateways] 1346. Kumar Trivedi, M. (2015), Evaluation of Vegetative Growth Parameters in Biofield Treated Bottle Gourd (Lagenaria siceraria) and Okra (Abelmoschus esculentus), IJNFS, 4(6), 688, doi:10.11648/j.ijnfs.20150406.24. (published) [Science Gateways] 1347. Kumar Trivedi, M., R. M. Tallapragada, and A. Branton (2015), Influence of Biofield Treatment on Physical and Structural Characteristics of Barium Oxide and Zinc Sulfide, J Laser Opt Photonics, 02(02), doi:10.4172/2469-410x.1000122. (published) [Science Gateways] 1348. Trivedi, M. K., R. M. Tallapragada, and A. B. Dahryn Trivedi (2015), Characterization of Physical and Structural Properties of Aluminium Carbide Powder: Impact of Biofield Treatment, Journal of Aeronautics & Aerospace Engineering, 04(01), doi:10.4172/2168-9792.1000142. (published) [Science Gateways] 1349. Jana, S., M. K. Trivedi, and A. Branton (2015), Characterization of Phenotype and Genotype of Biofield Treated Enterobacter aerogenes, Translational Medicine, 05(04), doi:10.4172/2161-1025.1000155. (published) [Science Gateways] 1350. Trivedi, M. K. (2015), Characterization of Physical, Thermal and Structural Properties of Chromium (VI) Oxide Powder: Impact of Biofield Treatment, Journal of Powder Metallurgy & Mining, 04(01), doi:10.4172/2168-9806.1000128. (published) [Science Gateways]
PY6 IPR 1 Page 160 1351. Webb, R., Bonifas, A., Behnaz, A., Zhang, Y., Yu, K., et al. 2013. Ultrathin conformal devices for precise and continuous thermal characterization of human skin. Nature materials 12: 938--944. (published) 1352. Weiß, A. et al. (2013), ALMA REDSHIFTS OF MILLIMETER-SELECTED GALAXIES FROM THE SPT SURVEY: THE REDSHIFT DISTRIBUTION OF DUSTY STAR-FORMING GALAXIES, The Astrophysical Journal, 767(1), 88, doi:10.1088/0004-637x/767/1/88. (published) 1353. Welikala, N. et al. (2015), Probing star formation in the dense environments of z 1 lensing haloes aligned with dusty star-forming galaxies detected with the South Pole Telescope, Monthly Notices of the Royal Astronomical Society, 455(2), 1629–1646, doi:10.1093/mnras/stv2302. (published) 1354. Xia, F., Kim, S., Cheng, H., Lee, J., Song, T., et al. 2013. Facile synthesis of free-standing silicon membranes with three-dimensional nanoarchitecture for anodes of lithium ion batteries. Nano letters 13: 3340--3346. (published) 1355. Xu, L., Gutbrod, S., Bonifas, A., Su, Y., Sulkin, M., et al. 2014. 3D multifunctional integumentary membranes for spatiotemporal cardiac measurements and stimulation across the entire epicardium. Nature communications 5. (published) 1356. Xu, S., Yan, Z., Jang, K., Huang, W., Fu, H., et al. 2015. Assembly of micro/nanomaterials into complex, three- dimensional architectures by compressive buckling. Science 347: 154--159. (published) 1357. Xu, S., Zhang, Y., Cho, J., Lee, J., Huang, X., et al. 2013. Stretchable batteries with self-similar serpentine interconnects and integrated wireless recharging systems. Nature Communications 4: 1543. (published) 1358. Yin, L., Cheng, H., Mao, S., Haasch, R., Liu, Y., et al. 2014. Transient Electronics: Dissolvable Metals for Transient Electronics (Adv. Funct. Mater. 5/2014). Advanced Functional Materials 24: 644--644. (published) 1359. Yudelson, M., Hosseini, R., Vihavainen, A., Brusilovsky, P. 2014. Investigating Automated Student Modeling in a Java MOOC. Educational Data Mining 2014 (London, UK). www.educationaldatamining.org/EDM2014/. (published) 1360. Yu, K., Kuzum, D., Hwang, S., Kim, B., Juul, H., et al. 2016. Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex. Nature materials. (published) 1361. Yuzbashyan, E. A., B. S. Shastry, and J. A. Scaramazza (2016), Rotationally invariant ensembles of integrable matrices, Phys. Rev. E, 93(5), doi:10.1103/physreve.93.052114. (published) 1362. Zhang, Y., Fu, H., Su, Y., Xu, S., Cheng, H., et al. 2013. Mechanics of ultra-stretchable self-similar serpentine interconnects. Acta Materialia 61: 7816--7827. (published) 1363. Žitko, R., D. Hansen, E. Perepelitsky, J. Mravlje, A. Georges, and B. S. Shastry (2013), Extremely correlated Fermi liquid theory meets dynamical mean-field theory: Analytical insights into the doping-driven Mott transition, Physical Review B, 88(23), doi:10.1103/physrevb.88.235132. (published)
PY6 IPR 1 Page 161 11. Service Provider Forum Report Service Providers (SPs) are independently funded projects or organizations that provide cyberinfrastructure (CI) services to the science and engineering community. In the US academic community, there is a rich diversity of Service Providers, spanning centers that are funded by NSF to operate large-scale resources for the national research community to universities that provide resources and services to their local researchers. The Service Provider Forum is intended to facilitate this ecosystem of Service Providers, thereby advancing the science and engineering researchers that rely on these cyberinfrastructure services. The Service Provider Forum (SPF) has two primary elements of its charter: ● An open forum for discussion of topics of interest to the SP community. ● A formal communication channel between the SPF members and the XSEDE project. This report is a quarterly summary of the SP Forum’s activities only. Service Providers that are required to submit quarterly reports do so directly to NSF, based on guidance from their NSF Program Officer. The SPF conducts its business primarily through regular biweekly conference calls on Thursdays at 4PM Eastern Time. Agendas are distributed in advance of the meetings and minutes are maintained on the XSEDE SP wiki5. NSF Program Officers are invited and often participate. Many people from the XSEDE program routinely participate in the SP Forum meetings to facilitate direct interaction with the XSEDE program, including John Towns (XSEDE PI), Victor Hazlewood (XSEDE SP Coordinator), and Ken Hackworth (XSEDE Allocations Coordinator). Additional contributors from XSEDE, XD-TAS and other organizations are frequently invited to lead or support specific agenda topics. In addition to the teleconference meetings, the SPF also convened an in-person session at the XSEDE-16 conference in Miami in July. Below is a summary of the SP Forum’s main activities in this reporting quarter. Technical and XSEDE specific discussions Calls this last quarter were dominated by discussions of the new structure of XSEDE-2, as many of the L2 and L3 managers gave presentations on their areas of responsibility and gathered feedback, including CEE, XCI, ECSS, and XRAS. Discussions were held about extending the XSEDE sign on hub for use be level 3 providers, about the creation of a shared virtual machine repository for use between the Jetstream and Bridges resources, the use of Speedpage for network testing, and around the evolving definition of service units and how this might be reflected in allocation policies. New Federation members The SPF continued to grow – The University of Houston applied to join as a Level 3 member, Bridges was accepted as a Level 1 member, and NICS (whose L1 membership had expired) re- joined as a Level 3 member.
5 https://www.xsede.org/group/sp-forum/wiki
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SP XF INSTITUTION PI ALTERNATE(S) ESTIMATED RESOURCE/SERVIC LEVEL RESOURCE E END DATE Greenfield 1 PSC Mike Levine Bob Stock, J Ray Scott, 12/31/2015 Nick Nystrom Comet 1 UCSD/SDSC Mike Norman Richard Moore, Bob 3/2019 Sinkovits Darter/Nautilus 1 Univ of Greg Peterson Justin Whitt 6/1/2016 Tennessee- Knoxville/NICS Maverick 1 Univ of Texas- Kelly Gaither Chris Hempel 8/1/2016 Austin/TACC Stampede 1 Univ of Texas- Dan Stanzione Chris Hempel 1/5/2017 Austin/TACC Wrangler 1 Univ of Texas- Dan Stanzione Niall Gaffney 2/2019 Austin/TACC JetStream 1 Indiana University Craig Stewart David Hancock 1/2020 and TACC Blue Waters 2 UIUC/NCSA William Kjellrun Olson, Mike 8/2018 Kramer Showerman OSG 2 Univ of Wisconsin Miron Livny Dan Fraser 05/30/2017 Rosen Ctr 2 Purdue Carol Song Preston Smith n/a SuperMIC 2 Louisiana State U Honggao Liu Sam White, Jim Lupo ?? XSEDEnet, Globus 2 NCAR Rich Loft Dave Hart n/a Online/GLADE Arkansas High 3 U of Arkansas Jeff Pummill TBD n/a Performance Computing Center (AHPCC) DataONE 3 Univ New Mexico Bruce Wilson Rebecca Koskela n/a + UTK, UCSB, ... Inst for Cyber- 3 Michigan State Kennie Merz Dirk Colbry n/a Enabled Res (iCER) University Inst for 3 Kansas State Daniel TBD n/a Computational University Andresen Researd in Engin & Science (ICRES) Minnesota 3 U of Minnesota Jeffrey TBD n/a Supercomputing McDonald Institute (MSI) OSCER 3 U of Oklahoma Henry Neeman TBD n/a OSU High- 3 Oklahoma State Dana Brunson TBD n/a Performance Comp University Ctr
PY6 IPR 1 Page 163 Rutgers 3 Rutgers University Manish Prentice Bisbal n/a Parashar USDA-PBARC 3 USDA (Hawaii) Brian Hall Scott Geib n/a FutureSystems 3 Indiana Univ Geoffrey Fox Gary Miksik n/a GA Tech 3 Georgia Tech Jeff Vetter Jeff Young n/a NCSA 3 Univ of Illinois John Towns TBD n/a Urbana- Champaign Tufts 3 Tufts Shawn Doughty TBD n/a Georgia State 3 Georgia State Suranga TBD n/a Edirisinghe Holland Computing 3 U. of David Swanson TBD n/a Center Nebraska/Lincoln Palmetto 3 Clemson Barr Von TBD n/a University Oehson ARC-TS (Advanced 3 University of Brock Palen TBD n/a Research Computing Michigan – Technology Services) LUCCRE 3 Langston Fondjo-Fotou TBD n/a University Franklin
PY6 IPR 1 Page 164 12. TAS Summary 12.1. Executive Summary Since the introduction of job level performance data into Open XDMoD in the 4th quarter of 2015, we have continued to see an increase in its adoption by academic and industrial HPC centers worldwide – providing strong evidence of the desire of HPC support personnel for a tool to measure the efficiency of end user jobs, with a goal of improving their performance. Accordingly, during the current reporting period, a great deal of work by the XMS team focused on improving the functionality and ease of use of a key feature of XDMoD and Open XDMoD, the Job Viewer. In addition, owing to the general difficulty in tracking memory usage, high water memory metrics were added to improve the ability to profile memory usage. Progress was made on the project to incorporate Ganglia job level performance data into Open XDMoD instances, which represents the 3rd monitoring framework that is compatible with Open XDMoD (performance coPilot and TACC_stats being the other two). In terms of XSEDE, two key improvements were made in the data sources that are used to populate data into XDMoD for subsequent analysis. First, the Resource Allocation realm now draws its data directly from XRAS ensuring that the most recent and accurate data is available to XDMoD users. Secondly, XDMoD now obtains its information about XSEDE resources from the new RDR API ensuring that each XSEDE HPC resource is more accurately defined in terms of its configuration. Finally, progress was made on the further development of scientific impact metrics for XSEDE. Progress was also made on two programs that are intimately tied to the XMS program. These programs share personnel resources with XMS and ultimately will contribute significantly to the capability of Open XDMoD, namely the XDMoD Value Analytics EAGER award and the Blue Waters Workload Analysis EAGER award. For the XDMoD Value Analytics program, a new Value Analytics realm was added as a “Grants realm”, which is the first step in adding the ability for Open XDMoD to report return on investment (ROI) metrics that are based on external grant funding by the researchers utilizing a given HPC resource. The Blue Water’s workload analysis is progressing and when completed, as a side benefit, Blue Water’s Open XDMoD production instance will have the ability to directly measure job level performance for all jobs running on Blue Waters. 12.2. XMS Findings: 1. XDMoD Release 6.0 contains several improvements and new features including: improvements to the Job Viewer and adding high water memory metrics to the job level performance data to better profile memory usage. In addition, two key improvements were made in the data sources which will improve the reliability and uniformity of the XDMoD information. First, the Resource Allocations realm now draws its data directly from the newly modified XRAS. Second, XDMoD utilizes the new RDR JSON API as its source for resource infrastructure. 2. Open XDMoD is widely used nationally and internationally at academic and industrial HPC centers, including governmental agencies such as NIH, the U.S. Center for Disease Control and Prevention, and most recently the Department of Defense 3. Since the release of Open XDMoD 5.5 (December 18th 2015), which included the ability
PY6 IPR 1 Page 165 for users and system support personnel to measure job level performance there have been 1940 downloads and 47 known installs of Open XDMoD version 5.5 or higher as of September 30, 2016.
12.3. XMS Recommendations: 1. XSEDE and the XD Net Service Providers should leverage CCR’s on-going work on the development of metrics for cloud instances for the development of cloud metrics appropriate for XSEDE. This work also will be applicable to Open XDMoD users running cloud or mixed cloud and HPC facilities. 2. XMS should work with TACC to design and collect appropriate metrics and analytics for TACC Wrangler, which currently does not correctly report usage. 3. With the incorporation of SUPReMM job level performance data into XDMoD and Open XDMoD and the collection of job level performance data by the largest XSEDE compute facilities, details on CPU usage, memory usage, network activity and I/O usage are now readily available. It is recommended that XSEDE’s ECSS team, with training from XMS, leverage the job level performance data that is available through the Job Viewer tab in XDMoD to work with end-users to improve their application’s operational performance. 4. Conspicuously missing from the XDMoD data is information regarding data storage by the end-users. Accordingly, it is recommended that the XSEDE service providers work with XMS to implement storage tracking utilities.
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