High-Performance I/O Programming Models for Exascale Computing
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2020 ALCF Science Report
ARGONNE LEADERSHIP 2020 COMPUTING FACILITY Science On the cover: A snapshot of a visualization of the SARS-CoV-2 viral envelope comprising 305 million atoms. A multi-institutional research team used multiple supercomputing resources, including the ALCF’s Theta system, to optimize codes in preparation for large-scale simulations of the SARS-CoV-2 spike protein that were recognized with the ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research. Image: Rommie Amaro, Lorenzo Casalino, Abigail Dommer, and Zied Gaieb, University of California San Diego 2020 SCIENCE CONTENTS 03 Message from ALCF Leadership 04 Argonne Leadership Computing Facility 10 Advancing Science with HPC 06 About ALCF 12 ALCF Resources Contribute to Fight Against COVID-19 07 ALCF Team 16 Edge Services Propel Data-Driven Science 08 ALCF Computing Resources 18 Preparing for Science in the Exascale Era 26 Science 28 Accessing ALCF GPCNeT: Designing a Benchmark 43 Materials Science 51 Physics Resources for Science Suite for Inducing and Measuring Constructing and Navigating Hadronic Light-by-Light Scattering Contention in HPC Networks Polymorphic Landscapes of and Vacuum Polarization Sudheer Chunduri Molecular Crystals Contributions to the Muon 30 2020 Science Highlights Parallel Relational Algebra for Alexandre Tkatchenko Anomalous Magnetic Moment Thomas Blum 31 Biological Sciences Logical Inferencing at Scale Data-Driven Materials Sidharth Kumar Scalable Reinforcement-Learning- Discovery for Optoelectronic The Last Journey Based Neural Architecture Search Applications -
Forge and Performance Reports Modules $ Module Load Intel Intelmpi $ Module Use /P/Scratch/Share/VI-HPS/JURECA/Mf/ $ Module Load Arm-Forge Arm-Reports
Acting on Insight Tips for developing and optimizing scientific applications [email protected] 28/06/2019 Agenda • Introduction • Maximize application efficiency • Analyze code performance • Profile multi-threaded codes • Optimize Python-based applications • Visualize code regions with Caliper 2 © 2019 Arm Limited Arm Technology Already Connects the World Arm is ubiquitous Partnership is key Choice is good 21 billion chips sold by We design IP, we do not One size is not always the best fit partners in 2017 manufacture chips for all #1 in Infrastructure today with Partners build products for HPC is a great fit for 28% market shares their target markets co-design and collaboration 3 © 2019 Arm Limited Arm’s solution for HPC application development and porting Combines cross-platform tools with Arm only tools for a comprehensive solution Cross-platform Tools Arm Architecture Tools FORGE C/C++ & FORTRAN DDT MAP COMPILER PERFORMANCE PERFORMANCE REPORTS LIBRARIES 4 © 2019 Arm Limited The billion dollar question in “weather and forecasting” Is it going to rain tomorrow? 1. Choose domain 2. Gather Data 3. Create Mesh 4. Match Data to Mesh 5. Simulate 6. Visualize 5 © 2019 Arm Limited Weather forecasting workflow Deploy Production Staging environment Builds Scalability Performance Develop Fix Regressions CI Agents Optimize Commit Continuous Version Start CI job control integration Pull system framework • 24 hour timeframe 6 © 2019 Arm Limited • 2 to 3 test runs for 1 production run Application efficiency Scientist Developer System admin Decision maker • Efficient use of allocation • Characterize application • Maximize resource usage • High-level view of system time behaviour • Diagnose performance workload • Higher result throughput • Gets hints on next issues • Reporting figures and optimization steps analysis to help decision making 7 © 2019 Arm Limited Arm Performance Reports Characterize and understand the performance of HPC application runs Gathers a rich set of data • Analyses metrics around CPU, memory, IO, hardware counters, etc. -
Adaptive Data Migration in Load-Imbalanced HPC Applications
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 10-16-2020 Adaptive Data Migration in Load-Imbalanced HPC Applications Parsa Amini Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Computer Sciences Commons Recommended Citation Amini, Parsa, "Adaptive Data Migration in Load-Imbalanced HPC Applications" (2020). LSU Doctoral Dissertations. 5370. https://digitalcommons.lsu.edu/gradschool_dissertations/5370 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. ADAPTIVE DATA MIGRATION IN LOAD-IMBALANCED HPC APPLICATIONS A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Computer Science by Parsa Amini B.S., Shahed University, 2013 M.S., New Mexico State University, 2015 December 2020 Acknowledgments This effort has been possible, thanks to the involvement and assistance of numerous people. First and foremost, I thank my advisor, Dr. Hartmut Kaiser, who made this journey possible with their invaluable support, precise guidance, and generous sharing of expertise. It has been a great privilege and opportunity for me be your student, a part of the STE||AR group, and the HPX development effort. I would also like to thank my mentor and former advisor at New Mexico State University, Dr. -
Fairborn Camera & Video
© 2006 The Dayton Microcomputer Association, Inc. V O L UM E 3 1 , I S S U E 6 P A G E 1 TM Volume 31 Issue 6 www.DMA.org November 2006 Association of PC User Groups (APCUG) Member Location for October 31 General Meeting Topic meeting & map inside ... Parking Permits Fairborn Camera & Video Available … Mike Petros - Guest Speaker As the holidays approach, the search be- for prints. RAW format allows the option of gins for the hottest items for Christmas. We manipulating details with photo-editing soft- generally look for some high-tech gadget ware on a PC. It’s a good thing that memory that could make our lives easier, more pro- cards hold more data than ever before. ductive, or just plain fun. The latest in cam- Mike Petros, Store Manager at Fairborn era equipment has always been a favorite Camera & Video, has agreed to demonstrate and the choices this year are impressive. several of this year’s latest digital cameras There seem to be a half dozen models for and help make sense of their many features. every application. Credit-card sized “point Mike draws from 30 years of experience in and shoot” cameras fit easily in a pocket and the business. He often gives presentations to travel well. Those best suited for sports pho- local organizations and once wrote articles tography are the ones marked “single-lens- for the Midwest PC Review magazine. reflex”. They tend to be more responsive Although he would not give away any de- and accept interchangeable lenses. Cameras tails on the specials they will be running this of all sizes offer digital viewfinders. -
Biology at the Exascale
Biology at the Exascale Advances in computational hardware and algorithms that have transformed areas of physics and engineering have recently brought similar benefits to biology and biomedical research. Contributors: Laura Wolf and Dr. Gail W. Pieper, Argonne National Laboratory Biological sciences are undergoing a revolution. High‐performance computing has accelerated the transition from hypothesis‐driven to design‐driven research at all scales, and computational simulation of biological systems is now driving the direction of biological experimentation and the generation of insights. As recently as ten years ago, success in predicting how proteins assume their intricate three‐dimensional forms was considered highly unlikely if there was no related protein of known structure. For those proteins whose sequence resembles a protein of known structure, the three‐dimensional structure of the known protein can be used as a “template” to deduce the unknown protein structure. At the time, about 60 percent of protein sequences arising from the genome sequencing projects had no homologs of known structure. In 2001, Rosetta, a computational technique developed by Dr. David Baker and colleagues at the Howard Hughes Medical Institute, successfully predicted the three‐dimensional structure of a folded protein from its linear sequence of amino acids. (Baker now develops tools to enable researchers to test new protein scaffolds, examine additional structural hypothesis regarding determinants of binding, and ultimately design proteins that tightly bind endogenous cellular proteins.) Two years later, a thirteen‐year project to sequence the human genome was declared a success, making available to scientists worldwide the billions of letters of DNA to conduct postgenomic research, including annotating the human genome. -
Performance Tuning Workshop
Performance Tuning Workshop Samuel Khuvis Scientifc Applications Engineer, OSC February 18, 2021 1/103 Workshop Set up I Workshop – set up account at my.osc.edu I If you already have an OSC account, sign in to my.osc.edu I Go to Project I Project access request I PROJECT CODE = PZS1010 I Reset your password I Slides are the workshop website: https://www.osc.edu/~skhuvis/opt21_spring 2/103 Outline I Introduction I Debugging I Hardware overview I Performance measurement and analysis I Help from the compiler I Code tuning/optimization I Parallel computing 3/103 Introduction 4/103 Workshop Philosophy I Aim for “reasonably good” performance I Discuss performance tuning techniques common to most HPC architectures I Compiler options I Code modifcation I Focus on serial performance I Reduce time spent accessing memory I Parallel processing I Multithreading I MPI 5/103 Hands-on Code During this workshop, we will be using a code based on the HPCCG miniapp from Mantevo. I Performs Conjugate Gradient (CG) method on a 3D chimney domain. I CG is an iterative algorithm to numerically approximate the solution to a system of linear equations. I Run code with srun -n <numprocs> ./test_HPCCG nx ny nz, where nx, ny, and nz are the number of nodes in the x, y, and z dimension on each processor. I Download with: wget go . osu .edu/perftuning21 t a r x f perftuning21 I Make sure that the following modules are loaded: intel/19.0.5 mvapich2/2.3.3 6/103 More important than Performance! I Correctness of results I Code readability/maintainability I Portability - -
ECP Software Technology Capability Assessment Report
ECP-RPT-ST-0001-2018 ECP Software Technology Capability Assessment Report Michael A. Heroux, Director ECP ST Jonathan Carter, Deputy Director ECP ST Rajeev Thakur, Programming Models & Runtimes Lead Jeffrey Vetter, Development Tools Lead Lois Curfman McInnes, Mathematical Libraries Lead James Ahrens, Data & Visualization Lead J. Robert Neely, Software Ecosystem & Delivery Lead July 1, 2018 DOCUMENT AVAILABILITY Reports produced after January 1, 1996, are generally available free via US Department of Energy (DOE) SciTech Connect. Website http://www.osti.gov/scitech/ Reports produced before January 1, 1996, may be purchased by members of the public from the following source: National Technical Information Service 5285 Port Royal Road Springfield, VA 22161 Telephone 703-605-6000 (1-800-553-6847) TDD 703-487-4639 Fax 703-605-6900 E-mail [email protected] Website http://www.ntis.gov/help/ordermethods.aspx Reports are available to DOE employees, DOE contractors, Energy Technology Data Exchange representatives, and International Nuclear Information System representatives from the following source: Office of Scientific and Technical Information PO Box 62 Oak Ridge, TN 37831 Telephone 865-576-8401 Fax 865-576-5728 E-mail [email protected] Website http://www.osti.gov/contact.html This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. -
Experimental and Analytical Study of Xeon Phi Reliability
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Lume 5.8 Experimental and Analytical Study of Xeon Phi Reliability Daniel Oliveira Laércio Pilla Nathan DeBardeleben Institute of Informatics, UFRGS Department of Informatics and Los Alamos National Laboratory Porto Alegre, RS, Brazil Statistics, UFSC Los Alamos, NM, US Florianópolis, SC, Brazil Sean Blanchard Heather Quinn Israel Koren Los Alamos National Laboratory Los Alamos National Laboratory University of Massachusetts, UMass Los Alamos, NM, US Los Alamos, NM, US Amherst, MA, US Philippe Navaux Paolo Rech Institute of Informatics, UFRGS Institute of Informatics, UFRGS Porto Alegre, RS, Brazil Porto Alegre, RS, Brazil ABSTRACT 1 INTRODUCTION We present an in-depth analysis of transient faults effects on HPC Accelerators are extensively used to expedite calculations in large applications in Intel Xeon Phi processors based on radiation experi- HPC centers. Tianhe-2, Cori, Trinity, and Oakforest-PACS use Intel ments and high-level fault injection. Besides measuring the realistic Xeon Phi and many other top supercomputers use other forms of error rates of Xeon Phi, we quantify Silent Data Corruption (SDCs) accelerators [17]. The main reasons to use accelerators are their by correlating the distribution of corrupted elements in the out- high computational capacity, low cost, reduced per-task energy put to the application’s characteristics. We evaluate the benefits consumption, and flexible development platforms. Unfortunately, of imprecise computing for reducing the programs’ error rate. For accelerators are also extremely likely to experience transient errors example, for HotSpot a 0.5% tolerance in the output value reduces as they are built with cutting-edge technology, have very high the error rate by 85%. -
Performance Tuning Workshop
Performance Tuning Workshop Samuel Khuvis Scientific Applications Engineer, OSC 1/89 Workshop Set up I Workshop – set up account at my.osc.edu I If you already have an OSC account, sign in to my.osc.edu I Go to Project I Project access request I PROJECT CODE = PZS0724 I Slides are on event page: osc.edu/events I Workshop website: https://www.osc.edu/˜skhuvis/opt19 2/89 Outline I Introduction I Debugging I Hardware overview I Performance measurement and analysis I Help from the compiler I Code tuning/optimization I Parallel computing 3/89 Introduction 4/89 Workshop Philosophy I Aim for “reasonably good” performance I Discuss performance tuning techniques common to most HPC architectures I Compiler options I Code modification I Focus on serial performance I Reduce time spent accessing memory I Parallel processing I Multithreading I MPI 5/89 Hands-on Code During this workshop, we will be using a code based on the HPCCG miniapp from Mantevo. I Performs Conjugate Gradient (CG) method on a 3D chimney domain. I CG is an iterative algorithm to numerically approximate the solution to a system of linear equations. I Run code with mpiexec -np <numprocs> ./test HPCCG nx ny nz, where nx, ny, and nz are the number of nodes in the x, y, and z dimension on each processor. I Download with git clone [email protected]:khuvis.1/performance2019 handson.git I Make sure that the following modules are loaded: intel/18.0.3 mvapich2/2.3 6/89 More important than Performance! I Correctness of results I Code readability/maintainability I Portability - future systems -
Best Practice Guide Modern Processors
Best Practice Guide Modern Processors Ole Widar Saastad, University of Oslo, Norway Kristina Kapanova, NCSA, Bulgaria Stoyan Markov, NCSA, Bulgaria Cristian Morales, BSC, Spain Anastasiia Shamakina, HLRS, Germany Nick Johnson, EPCC, United Kingdom Ezhilmathi Krishnasamy, University of Luxembourg, Luxembourg Sebastien Varrette, University of Luxembourg, Luxembourg Hayk Shoukourian (Editor), LRZ, Germany Updated 5-5-2021 1 Best Practice Guide Modern Processors Table of Contents 1. Introduction .............................................................................................................................. 4 2. ARM Processors ....................................................................................................................... 6 2.1. Architecture ................................................................................................................... 6 2.1.1. Kunpeng 920 ....................................................................................................... 6 2.1.2. ThunderX2 .......................................................................................................... 7 2.1.3. NUMA architecture .............................................................................................. 9 2.2. Programming Environment ............................................................................................... 9 2.2.1. Compilers ........................................................................................................... 9 2.2.2. Vendor performance libraries -
Technical Challenges of Exascale Computing
Technical Challenges of Exascale Computing Contact: Dan McMorrow — [email protected] April 2013 JSR-12-310 Approved for public release; distribution unlimited. JASON The MITRE Corporation 7515 Colshire Drive McLean, Virginia 22102-7508 (703) 983-6997 Contents 1 ABSTRACT 1 2 EXECUTIVE SUMMARY 3 2.1 Overview . 4 2.2 Findings . 7 2.3 Recommendations . 9 3 DOE/NNSA COMPUTING CHALLENGES 11 3.1 Study Charge from DOE/NNSA . 11 3.2 Projected Configuration of an Exascale Computer . 13 3.3 Overview of DOE Exascale Computing Initiative . 15 3.4 The 2008 DARPA Study . 17 3.5 Overview of the Report . 19 4 HARDWARE CHALLENGES FOR EXASCALE COMPUTING 21 4.1 Evolution of Moore’s Law . 21 4.2 Evolution of Memory Size and Memory Bandwidth . 25 4.3 Memory Access Patterns of DOE/NNSA Applications . 32 4.4 The Roof-Line Model . 39 4.5 Energy Costs of Computation . 46 4.6 Memory Bandwidth and Energy . 48 4.7 Some Point Designs for Exascale Computers . 50 4.8 Resilience . 52 4.9 Storage . 57 4.9.1 Density . 58 4.9.2 Power . 60 4.9.3 Storage system reliability . 67 4.10 Summary and Conclusions . 71 5 REQUIREMENTS FOR DOE/NNSA APPLICATIONS 73 5.1 Climate Simulation . 73 5.2 Combustion . 78 5.3 NNSA Applications . 89 5.4 Summary and Conclusion . 90 iii 6 RESEARCH DIRECTIONS 93 6.1 Breaking the Memory Wall . 93 6.2 Role of Photonics for Exascale Computing . 96 6.3 Computation and Communication Patterns of DOE/NNSA Appli- cations . 99 6.4 Optimizing Hardware for Computational Patterns . -
The US DOE Exascale Computing Project (ECP) Perspective for the HEP Community
The US DOE Exascale Computing Project (ECP) Perspective for the HEP Community Douglas B. Kothe (ORNL), ECP Director Lori Diachin (LLNL), ECP Deputy Director Erik Draeger (LLNL), ECP Deputy Director of Application Development Tom Evans (ORNL), ECP Energy Applications Lead Blueprint Workshop on A Coordinated Ecosystem for HL-LHC Computing R&D Washington, DC October 23, 2019 DOE Exascale Program: The Exascale Computing Initiative (ECI) Three Major Components of the ECI ECI US DOE Office of Science (SC) and National partners Nuclear Security Administration (NNSA) Exascale Selected program Computing office application Project development ECI Accelerate R&D, acquisition, and deployment to (BER, BES, (ECP) deliver exascale computing capability to DOE NNSA) mission national labs by the early- to mid-2020s Exascale system ECI Delivery of an enduring and capable exascale procurement projects & computing capability for use by a wide range facilities focus of applications of importance to DOE and the US ALCF-3 (Aurora) OLCF-5 (Frontier) ASC ATS-4 (El Capitan) 2 ECP Mission and Vision Enable US revolutions in technology development; scientific discovery; healthcare; energy, economic, and national security ECP ECP mission vision Develop exascale-ready applications Deliver exascale simulation and and solutions that address currently data science innovations and intractable problems of strategic solutions to national problems importance and national interest. that enhance US economic competitiveness, change our quality Create and deploy an expanded and of life, and strengthen our national vertically integrated software stack on security. DOE HPC exascale and pre-exascale systems, defining the enduring US exascale ecosystem. Deliver US HPC vendor technology advances and deploy ECP products to DOE HPC pre-exascale and exascale systems.