Distributed Computing with the Berkeley Open Infrastructure for Network Computing BOINC
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Job Peer Group Controller Job Peer Group Host a PG for Each Job
Korea-Japan Grid Symposium, Seoul December 1-2, 2004 P3: Personal Power Plant Makes over your PCs into power generator on the Grid Kazuyuki Shudo <[email protected]>, Yoshio Tanaka, Satoshi Sekiguchi Grid Technology Research Center, AIST, Japan P3: Personal Power Plant Middleware for distributed computation utilizing JXTA. Traditional goals Cycle scavenging Harvest compute power of existing PCs in an organization. Conventional dist. computing Internet-wide distributed computing E.g. distributed.net, SETI@home Challenging goals Aggregate PCs and expose them as an integrated Grid resource. Integrate P3 with Grid middleware ? cf. Community Scheduler Framework Dealings and circulation of computational resources Transfer individual resources (C2C, C2B) and also aggregated resources (B2B). Transfer and aggregation of Other resources than processing power. individual resources Commercial dealings need a market and a system supporting it. P2P way of interaction between PCs P3 uses JXTA for all communications JXTA is a widely accepted P2P protocol, project and library, that provides common functions P2P software requires. P2P concepts supported by JXTA efficiently support P3: Ad-hoc self-organization PCs can discover and communicate with each other without pre-configuration. Discovery PCs dynamically discover each other and jobs without a central server. Peer Group PCs are grouped into job groups, in which PCs carry out code distribution, job control, and collective communication for parallel computation. Overlay Network Peer ID in JXTA is independent from physical IDs like IP addresses and MAC addresses. JXTA enables end-to-end bidirectional communication over NA(P)T and firewall (even if the FW allows only unidirectional HTTP). This function supports parallel processing in the message-passing model, not only master-worker model. -
Web Services and PC Grid
Web Services and PC Grid ONG Guan Sin <[email protected]> Grid Evangelist Singapore Computer Systems Ltd APBioNet/APAN 18 Jul 2006 Tera-scale Campus Grid @ NUS LATEST: CIO Award 2006 winner Harnessing existing computation capacity campus-wide, creating large-scale supercomputing capability Computers are aggregated through its gigabit network into a virtual supercomputing platform using United Devices Grid MP middleware Community grid by voluntary participation from depts Two-year collaboration project between NUS and SCS to develop applications and support community Number of Nodes Theoretical# Practical^ 1,042 (Sep 20, 2005) 4.5 TFlops 2.5 TFlops 3,000 (Planned - 2007) 13 TFlops 7.2 TFlops * Accumulated average CPU speed of 2.456GHz as at Sep 20, 2005 # Assuming 90% capacity effectively harnessed ^ Assuming 50% capacity effectively harnessed Copyright 2006 Singapore Computer Systems Limited 2 UD Grid MP Architecture Managed Grid Services Interface (MGSI) ± Web Services API 3 Command-line Interface Application Service 4 Simple Web Interface 5 Application Service Overview Application Service . Is a job submission and result retrieval program which provides users with a simple interface for performing work on the Grid . It is responsible for Splitting and Merging Application Data Features . Control the Grid MP Sever with SOAP or XML-RPC communications . SOAP and XML-RPC Communications are protected with SSL encryption . SOAP and XML-RPC are language and platform independent with many publicly available toolkits and libraries. User interface can be command-line, web-based, GUI, etc. Can be written to run on various operating systems MP Grid Services Interface (MGSI) . All Objects in the Grid MP platform can be controlled through the MGSI . -
Good Practice Guide No. 17 Distributed Computing for Metrology
NPL REPORT DEM-ES 006 Software Support for Metrology - Good Practice Guide No. 17 Distributed computing for metrology applications T J Esward, N J McCormick, K M Lawrence and M J Stevens NOT RESTRICTED March 2007 National Physical Laboratory | Hampton Road | Teddington | Middlesex | United Kingdom | TW11 0LW Switchboard 020 8977 3222 | NPL Helpline 020 8943 6880 | Fax 020 8943 6458 | www.npl.co.uk Software Support for Metrology Good Practice Guide No. 17 Distributed computing for metrology applications T J Esward, N J McCormick, K M Lawrence and M J Stevens Mathematics and Scientific Computing Group March 2007 ABSTRACT This guide aims to facilitate the effective use of distributed computing methods and techniques by other National Measurement System (NMS) programmes and by metrologists in general. It focuses on the needs of those developing applications for distributed computing systems, and on PC desktop grids in particular, and seeks to ensure that application developers are given enough knowledge of system issues to be able to appreciate what is needed for the optimum performance of their own applications. Within metrology, the use of more comprehensive and realistic mathematical models that require extensive computing resources for their solution is increasing. The motivation for the guide is that in several areas of metrology the computational requirements of such models are so demanding that there is a strong requirement for distributed processing using parallel computing on PC networks. Those who need to use such technology can benefit from guidance on how best to structure the models and software to allow the effective use of distributed computing. -
Print This Article
L{{b b Volume 1, No. 3, Sept-Oct 2010 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info A Simplified Network manager for Grid and Presenting the Grid as a Computation Providing Cloud M.Sudha* M.Monica Assistant Professor (Senior) Assistant Professor, School of Information Technology and Engineering, VIT School of Computer Science and Engineering, University INDIA VIT University INDIA [email protected] [email protected] Abstract: One of the common forms of distributed computing is grid computing. A grid uses the resources of many separate computers, loosely connected by a network, to solve large-scale computation problems. Our approach was as follows first computationally large data is split into a number of smaller, more manageable, working units secondly each work-unit is then sent to one member of the grid ,That member completes processing of that work-unit in its own and sends back the result. In this architecture, there needs to be at least one host that performs the task of assigning work-units, and then sending them, to a remote processor, as well as receive the results from remote processors. We call this unit as the Network Manager. In addition to this assigning, sending and receiving the work-units and results, there also is the need for a host that splits tasks into work-units and assimilates the received work units. We call this unit as the Task Broker, which we propose to design. On the server end, there is a program for processing module, splitter and assimilator (broker). -
BIOINFORMATICS APPLICATIONS NOTE Doi:10.1093/Bioinformatics/Btq011
Vol. 26 no. 5 2010, pages 705–707 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btq011 Databases and ontologies Advance Access publication January 19, 2010 ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes Thomas Dan Otto1,2,∗, Marcos Catanho1, Cristian Tristão3, Márcia Bezerra3, Renan Mathias Fernandes4, Guilherme Steinberger Elias4, Alexandre Capeletto Scaglia4, Bill Bovermann5, Viktors Berstis5, Sergio Lifschitz3, Antonio Basílio de Miranda1 and Wim Degrave1 1Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil, 2Pathogen Genomics, Wellcome Trust Genome Campus, Hinxton, UK, 3Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 4IBM Brasil, Hortolândia, São Paulo, Brazil and 5IBM, Austin, TX, USA Associate Editor: Alfonso Valencia ABSTRACT nomenclature or might have no value when inferred from previous Motivation: Many analyses in modern biological research are incorrectly annotated sequences. Hence, secondary databases based on comparisons between biological sequences, resulting such as Swiss-Prot (http://www.expasy.ch/sprot/), PFAM (http:// in functional, evolutionary and structural inferences. When large pfam.sanger.ac.uk) or KEGG (http://www.genome.ad.jp/kegg), to numbers of sequences are compared, heuristics are often used mention only a few, have been implemented to analyze specific resulting in a certain lack of accuracy. In order to improve functional aspects and to improve the annotation procedures and and validate results of such comparisons, we have performed results. radical all-against-all comparisons of 4 million protein sequences Dynamic programming algorithms, or a fast approximation, belonging to the RefSeq database, using an implementation of the have been successfully applied to biological sequence comparison Smith–Waterman algorithm. -
ETSI TR 102 659-1 V1.2.1 (2009-10) Technical Report
ETSI TR 102 659-1 V1.2.1 (2009-10) Technical Report GRID; Study of ICT Grid interoperability gaps; Part 1: Inventory of ICT Stakeholders 2 ETSI TR 102 659-1 V1.2.1 (2009-10) Reference RTR/GRID-0001-1[2] Keywords analysis, directory, ICT, interoperability, testing ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice Individual copies of the present document can be downloaded from: http://www.etsi.org The present document may be made available in more than one electronic version or in print. In any case of existing or perceived difference in contents between such versions, the reference version is the Portable Document Format (PDF). In case of dispute, the reference shall be the printing on ETSI printers of the PDF version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http://portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced except as authorized by written permission. The copyright and the foregoing restriction extend to reproduction in all media. -
Distributed Computing with the Berkeley Open Infrastructure for Network Computing BOINC
Distributed Computing with the Berkeley Open Infrastructure for Network Computing BOINC Eric Myers -19 Update COVID8 July 2020 1 September 2010 Mid-Hudson Linux Users Group 2 How BOINC Works …for like 8 to 12 hrs! BOINC Client BOINC is the software BOINC Server Windows framework that makes Linux Mac OS this all work. Linux 50+ separate projects (& Solaris, AIX, HP-UX, etc…) 1 September 2010 Mid-Hudson Valley Linux Users Group 3 BOINC Dataflow 1 September 2010 Mid-Hudson Valley Linux Users Group 4 http://setiathome.berkeley.edu SETI@home SETI@home is ”paused” in 2020 http://einstein.phys.uwm.edu/ or http://einsteinathome.orG Einstein@Home 78 new pulsars detected by 2020 7 Rosetta@home 1 September 2010 Mid-Hudson Valley Linux Users Group 8 http://www.worldcommunitygrid.orG/ World CommunityAs of 2010 Grid Active The Clean Energy Project - Phase 2 Help Cure Muscular Dystrophy – Phase 2 Funded and operated by IBM Help Fight Childhood Cancer Help Conquer Cancer Human Proteome Folding - Phase 2 Completed FightAIDS@Home Nutritious Rice for the World Intermittent AfricanClimate@Home Discovering Dengue Drugs - Together - Phase 2 Help Cure Muscular Dystrophy Influenza Antiviral Drug Search Genome Comparison The Clean Energy Project Help Defeat Cancer Discovering Dengue Drugs - Together Human Proteome Folding 1 September 2010 Mid-Hudson Valley Linux Users Group 9 To Join: 1. Download BOINC 2. Run BOINC ManaGer 3. Tools -> Add Project 1 September 2010 Mid-Hudson Valley Linux Users Group 10 Call to Action! Use your spare cycles to help fight COVID-19 World Community Grid Rosetta@home OpenPandemics - COVID-19 8 July 2020 HVopen 11 Rules and Policies Run BOINC only on authorized computers Run BOINC only on computers that you own, or for which you have obtained the owner’s permission. -
Volunteer Computing and BOINC
Volunteer Computing and BOINC David P. Anderson Space Sciences Lab UC Berkeley MAGIC October 5, 2016 Scientific Volunteer Consumer computing computing electronics BOINC ● Middleware for volunteer computing – Open-source, NSF-funded development – Community-maintained ● Server: used by scientists to make “projects” ● Client: runs on consumer devices – “attach” to projects – fetches/runs jobs in background Example projects ● Climateprediction.net ● Rosetta@home ● Einstein@home ● IBM World Community Grid ● CERN Current volunteered resources ● 500,000 active devices – BOINC + Folding@home ● 2.3M CPU cores, 290K GPUs ● 93 PetaFLOPS ● 85% Windows, 7% Mac, 7% Linux Performance potential ● 1 billion desktop/laptop PCs – CPUs: 10 ExaFLOPS – GPUs: 1,000 ExaFLOPS ● 5 billion smartphones – CPUs: 20 ExaFLOPS – GPUs: 1,500 ExaFLOPS Realistic potential ● Study: 5-10% of people who learn about VC would participate ● Devices compute 60% of the time ● So if we can tell the world about VC, could get 100 ExaFLOPS Cost cost of 1 TFLOPS/year 4.5 4 3.5 3 2.5 2 cost ($M) 1.5 1 0.5 0 CPU cluster Amazon EC2 BOINC BOINC job model ● An app can have many versions ● Submit jobs to apps, not versions ● The BOINC scheduler decides what version(s) to use in response to a particular request Per-platform apps ● Windows/Intel, 32 and 64 bit ● Mac OS X ● Linux/Intel ● Linux/ARM (works for Android too) Other types of apps ● Multicore ● GPU apps – CUDA (Nvidia) – CAL (AMD) – OpenCL (Nvidia, AMD, Intel) VM apps ● App is VM image + executable ● BOINC client interfaces via “Vbox -
Laureate List 2011 3 27.Indd
THE LAUREATE THE COMPUTERWORLD HONORS PROGRAM | 2011 SYBASE CONGRATULATES OUR 2011 COMPUTERWORLD HONORS LAUREATES Bulgarian Electricity System Operator Cherry Creek School District Citizens Financial Group Everything Everywhere Limited JAGTAG Kodak Imaging Network Korea Ministry of Public Administration and Security (MOPAS) Matrix Systems and Solutions Medihelp Mobikash Afrika Congratulations Municipality of Aarhus 2011 Computerworld Honors Program New York State Division of Homeland Security and Emergency Laureates, Finalists, and Services — NY-ALERT 21st Century Achievement Award Recipients Oasis Medical Solutions Philippine Social Security System (SSS) Rafael Belloso Chacin University (URBE) Royal Netherlands Institute for Sea Research State of Maine Department of Health and Human Services Ready for what’s next. Swiss Medical Group, Argentina The desire to make a difference is the fi rst step that leads to success. Booz Allen Hamilton, a leading strategy and technology consulting fi rm, is proud Turkish Derivatives Exchange, Inc. (TurkDEX) to recognize the 2011 Computerworld Honors Program honorees. Thanks to your vision, commitment, and innovative use of IT, you’re making the world a better place. www.boozallen.com Copyright © 2011 Sybase, Inc. All rights reserved. Unpublished rights reserved under U.S. copyright laws. Sybase, and the Sybase logo, are trademarks of Sybase, Inc. or its subsidiaries. ® indicates registration in the United States of America. SAP, and the SAP logo, are the trademarks or registered trademarks of SAP AG in Germany and in several other countries. All other trademarks are the property of their respective owners. BA11-157_Computerworld Ad_052011Final.indd 1 5/20/11 8:32 AM . THE JOURNAL OF THE COMPUTERWORLD INFORMATION TECHNOLOGY AWARDS FOUNDATION . -
IBM's Contributions Towards Achieving the United Nations Sustainable
IBM’s contributions towards achieving the United Nations Sustainable Development Goals November 2018 2 Contents Introduction ............................................................................................................................................................ 3 Products, services and solutions ........................................................................................................................... 5 Blockchain .......................................................................................................................................................... 5 Watson IoT ......................................................................................................................................................... 6 Watson Health .................................................................................................................................................... 8 Workforce diversity and inclusion ........................................................................................................................ 10 Women in the workplace .................................................................................................................................. 10 Diversity constituencies ................................................................................................................................... 11 Accessibility and persons with disabilities ..................................................................................................... -
Dcp : Ar Tt Covid-19
Distributed Computing in a Pandemic: A Review of Technologies available for Tackling COVID-19 A Preprint Jamie J. Alnasir Department of Computing Imperial College London, UK [email protected] May 15, 2021 Abstract The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Some important areas of focus are: vaccine development, designing or repurposing existing pharmacological agents for treatment by identifying druggable targets, predicting and diagnosing the disease, and tracking and reducing the spread. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures — various types of clusters, grids and clouds — that can be leveraged to performthese tasks at scale, at high-throughput,with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters, which aggregate their compute nodes using high- bandwidth networking and support a high-degree of inter-process communication, are ubiquitous across scientific research — they will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, arXiv:2010.04700v3 [cs.DC] 18 May 2021 and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world’s fastest supercom- puters, such as IBM’s SUMMIT — for ensemble docking high-throughput screening against SARS- CoV-2 targets for drug-repurposing,and high-throughputgeneanalysis — andSentinel, an XPE-Cray based system used to explore natural products. -
Analyzing Daily Computing Runtimes on the World Community Grid
https://doi.org/10.48009/3_iis_2020_289-297 Issues in Information Systems Volume 21, Issue 3, pp. 289-297, 2020 EXPLORING GRID COMPUTING & VOLUNTEER COMPUTING: ANALYZING DAILY COMPUTING RUNTIMES ON THE WORLD COMMUNITY GRID Kieran Raabe, Bloomsburg University of Pennsylvania, [email protected] Loreen M. Powell, Bloomsburg University of Pennsylvania, [email protected] ABSTRACT In the early 1990s, the concept of grid computing was little more than a simple comparison which stated that making computing power as accessible as electricity from a power grid. Today, grid computing is far more complex. Grid computing involves utilizing unused computing power from devices across the globe to run calculations and simulations and submit the results to move a project closer to its goal. Some projects, such as World Community Grid, work to achieve numerous goals, herein referred to as subprojects. The goal of this research is to explore grid computing and volunteer computing. Specifically, this research involved a daily collection of statistics on daily results returned to World Community Grid and daily computing runtimes for three subprojects named Africa Rainfall Project, Microbiome Immunity Project, and Help Stop TB. The data collection lasted four weeks and resulted in five graphs for each subproject being created with the collected data. Results revealed a correlation between daily results returned and daily runtimes, with each data point for results returned being slightly lower than the runtime data point from the same day. Keywords: Grid Computing, Volunteer Computing, BOINC, World Community Grid INTRODUCTION The concept of grid computing has existed since the early 1990s. Originally “a metaphor for making computer power as easy to access by the user as electricity from a power grid” (Dutton & Jeffreys, 2010).