Highlights of the 55Th TOP500 List
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Interconnect Your Future Enabling the Best Datacenter Return on Investment
Interconnect Your Future Enabling the Best Datacenter Return on Investment TOP500 Supercomputers, November 2016 Mellanox Accelerates The World’s Fastest Supercomputers . Accelerates the #1 Supercomputer . 39% of Overall TOP500 Systems (194 Systems) . InfiniBand Connects 65% of the TOP500 HPC Platforms . InfiniBand Connects 46% of the Total Petascale Systems . Connects All of 40G Ethernet Systems . Connects The First 100G Ethernet System on The List (Mellanox End-to-End) . Chosen for 65 End-User TOP500 HPC Projects in 2016, 3.6X Higher versus Omni-Path, 5X Higher versus Cray Aries InfiniBand is the Interconnect of Choice for HPC Infrastructures Enabling Machine Learning, High-Performance, Web 2.0, Cloud, Storage, Big Data Applications © 2016 Mellanox Technologies 2 Mellanox Connects the World’s Fastest Supercomputer National Supercomputing Center in Wuxi, China #1 on the TOP500 List . 93 Petaflop performance, 3X higher versus #2 on the TOP500 . 41K nodes, 10 million cores, 256 cores per CPU . Mellanox adapter and switch solutions * Source: “Report on the Sunway TaihuLight System”, Jack Dongarra (University of Tennessee) , June 20, 2016 (Tech Report UT-EECS-16-742) © 2016 Mellanox Technologies 3 Mellanox In the TOP500 . Connects the world fastest supercomputer, 93 Petaflops, 41 thousand nodes, and more than 10 million CPU cores . Fastest interconnect solution, 100Gb/s throughput, 200 million messages per second, 0.6usec end-to-end latency . Broadest adoption in HPC platforms , connects 65% of the HPC platforms, and 39% of the overall TOP500 systems . Preferred solution for Petascale systems, Connects 46% of the Petascale systems on the TOP500 list . Connects all the 40G Ethernet systems and the first 100G Ethernet system on the list (Mellanox end-to-end) . -
Linpack Evaluation on a Supercomputer with Heterogeneous Accelerators
Linpack Evaluation on a Supercomputer with Heterogeneous Accelerators Toshio Endo Akira Nukada Graduate School of Information Science and Engineering Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Institute of Technology Tokyo, Japan Tokyo, Japan [email protected] [email protected] Satoshi Matsuoka Naoya Maruyama Global Scientific Information and Computing Center Global Scientific Information and Computing Center Tokyo Institute of Technology/National Institute of Informatics Tokyo Institute of Technology Tokyo, Japan Tokyo, Japan [email protected] [email protected] Abstract—We report Linpack benchmark results on the Roadrunner or other systems described above, it includes TSUBAME supercomputer, a large scale heterogeneous system two types of accelerators. This is due to incremental upgrade equipped with NVIDIA Tesla GPUs and ClearSpeed SIMD of the system, which has been the case in commodity CPU accelerators. With all of 10,480 Opteron cores, 640 Xeon cores, 648 ClearSpeed accelerators and 624 NVIDIA Tesla GPUs, clusters; they may have processors with different speeds as we have achieved 87.01TFlops, which is the third record as a result of incremental upgrade. In this paper, we present a heterogeneous system in the world. This paper describes a Linpack implementation and evaluation results on TSUB- careful tuning and load balancing method required to achieve AME with 10,480 Opteron cores, 624 Tesla GPUs and 648 this performance. On the other hand, since the peak speed is ClearSpeed accelerators. In the evaluation, we also used a 163 TFlops, the efficiency is 53%, which is lower than other systems. -
Tsubame 2.5 Towards 3.0 and Beyond to Exascale
Being Very Green with Tsubame 2.5 towards 3.0 and beyond to Exascale Satoshi Matsuoka Professor Global Scientific Information and Computing (GSIC) Center Tokyo Institute of Technology ACM Fellow / SC13 Tech Program Chair NVIDIA Theater Presentation 2013/11/19 Denver, Colorado TSUBAME2.0 NEC Confidential TSUBAME2.0 Nov. 1, 2010 “The Greenest Production Supercomputer in the World” TSUBAME 2.0 New Development >600TB/s Mem BW 220Tbps NW >12TB/s Mem BW >400GB/s Mem BW >1.6TB/s Mem BW Bisecion BW 80Gbps NW BW 35KW Max 1.4MW Max 32nm 40nm ~1KW max 3 Performance Comparison of CPU vs. GPU 1750 GPU 200 GPU ] 1500 160 1250 GByte/s 1000 120 750 80 500 CPU CPU 250 40 Peak Performance [GFLOPS] Performance Peak 0 Memory Bandwidth [ Bandwidth Memory 0 x5-6 socket-to-socket advantage in both compute and memory bandwidth, Same power (200W GPU vs. 200W CPU+memory+NW+…) NEC Confidential TSUBAME2.0 Compute Node 1.6 Tflops Thin 400GB/s Productized Node Mem BW as HP 80GBps NW ProLiant Infiniband QDR x2 (80Gbps) ~1KW max SL390s HP SL390G7 (Developed for TSUBAME 2.0) GPU: NVIDIA Fermi M2050 x 3 515GFlops, 3GByte memory /GPU CPU: Intel Westmere-EP 2.93GHz x2 (12cores/node) Multi I/O chips, 72 PCI-e (16 x 4 + 4 x 2) lanes --- 3GPUs + 2 IB QDR Memory: 54, 96 GB DDR3-1333 SSD:60GBx2, 120GBx2 Total Perf 2.4PFlops Mem: ~100TB NEC Confidential SSD: ~200TB 4-1 2010: TSUBAME2.0 as No.1 in Japan > All Other Japanese Centers on the Top500 COMBINED 2.3 PetaFlops Total 2.4 Petaflops #4 Top500, Nov. -
Performance Analysis on Energy Efficient High
Performance Analysis on Energy Efficient High-Performance Architectures Roman Iakymchuk, François Trahay To cite this version: Roman Iakymchuk, François Trahay. Performance Analysis on Energy Efficient High-Performance Architectures. CC’13 : International Conference on Cluster Computing, Jun 2013, Lviv, Ukraine. hal-00865845 HAL Id: hal-00865845 https://hal.archives-ouvertes.fr/hal-00865845 Submitted on 25 Sep 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Performance Analysis on Energy Ecient High-Performance Architectures Roman Iakymchuk1 and François Trahay1 1Institut Mines-Télécom Télécom SudParis 9 Rue Charles Fourier 91000 Évry France {roman.iakymchuk, francois.trahay}@telecom-sudparis.eu Abstract. With the shift in high-performance computing (HPC) towards energy ecient hardware architectures such as accelerators (NVIDIA GPUs) and embedded systems (ARM processors), arose the need to adapt existing perfor- mance analysis tools to these new systems. We present EZTrace a performance analysis framework for parallel applications. EZTrace relies on several core components, in particular on a mechanism for instrumenting func- tions, a lightweight tool for recording events, and a generic interface for writing traces. To support EZTrace on energy ecient HPC systems, we developed a CUDA module and ported EZTrace to ARM processors. -
Science-Driven Development of Supercomputer Fugaku
Special Contribution Science-driven Development of Supercomputer Fugaku Hirofumi Tomita Flagship 2020 Project, Team Leader RIKEN Center for Computational Science In this article, I would like to take a historical look at activities on the application side during supercomputer Fugaku’s climb to the top of supercomputer rankings as seen from a vantage point in early July 2020. I would like to describe, in particular, the change in mindset that took place on the application side along the path toward Fugaku’s top ranking in four benchmarks in 2020 that all began with the Application Working Group and Computer Architecture/Compiler/ System Software Working Group in 2011. Somewhere along this path, the application side joined forces with the architecture/system side based on the keyword “co-design.” During this journey, there were times when our opinions clashed and when efforts to solve problems came to a halt, but there were also times when we took bold steps in a unified manner to surmount difficult obstacles. I will leave the description of specific technical debates to other articles, but here, I would like to describe the flow of Fugaku development from the application side based heavily on a “science-driven” approach along with some of my personal opinions. Actually, we are only halfway along this path. We look forward to the many scientific achievements and so- lutions to social problems that Fugaku is expected to bring about. 1. Introduction In this article, I would like to take a look back at On June 22, 2020, the supercomputer Fugaku the flow along the path toward Fugaku’s top ranking became the first supercomputer in history to simul- as seen from the application side from a vantage point taneously rank No. -
Towards Exascale Computing
TowardsTowards ExascaleExascale Computing:Computing: TheThe ECOSCALEECOSCALE ApproachApproach Dirk Koch, The University of Manchester, UK ([email protected]) 1 Motivation: let’s build a 1,000,000,000,000,000,000 FLOPS Computer (Exascale computing: 1018 FLOPS = one quintillion or a billion billion floating-point calculations per sec.) 2 1,000,000,000,000,000,000 FLOPS . 10,000,000,000,000,000,00 FLOPS 1975: MOS 6502 (Commodore 64, BBC Micro) 3 Sunway TaihuLight Supercomputer . 2016 (fully operational) . 12,543,6000,000,000,000,00 FLOPS (125.436 petaFLOPS) . Architecture Sunway SW26010 260C (Digital Alpha clone) 1.45GHz 10,649,600 cores . Power “The cooling system for TaihuLight uses a closed- coupled chilled water outfit suited for 28 MW with a custom liquid cooling unit”* *https://www.nextplatform.com/2016/06/20/look-inside-chinas-chart-topping-new-supercomputer/ . Cost US$ ~$270 million 4 TOP500 Performance Development We need more than all the performance of all TOP500 machines together! 5 TaihuLight for Exascale Computing? We need 8x the worlds fastest supercomputer: . Architecture Sunway SW26010 260C (Digital Alpha clone) @1.45GHz: > 85M cores . Power 224 MW (including cooling) costs ~ US$ 40K/hour, US$ 340M/year from coal: 2,302,195 tons of CO2 per year . Cost US$ 2.16 billion We have to get at least 10x better in energy efficiency 2-3x better in cost Also: scalable programming models 6 Alternative: Green500 Shoubu supercomputer (#1 Green500 in 2015): . Cores: 1,181,952 . Theoretical Peak: 1,535.83 TFLOPS/s . Memory: 82 TB . Processor: Xeon E5-2618Lv3 8C 2.3GHz . -
Computational PHYSICS Shuai Dong
Computational physiCs Shuai Dong Evolution: Is this our final end-result? Outline • Brief history of computers • Supercomputers • Brief introduction of computational science • Some basic concepts, tools, examples Birth of Computational Science (Physics) The first electronic general-purpose computer: Constructed in Moore School of Electrical Engineering, University of Pennsylvania, 1946 ENIAC: Electronic Numerical Integrator And Computer ENIAC Electronic Numerical Integrator And Computer • Design and construction was financed by the United States Army. • Designed to calculate artillery firing tables for the United States Army's Ballistic Research Laboratory. • It was heralded in the press as a "Giant Brain". • It had a speed of one thousand times that of electro- mechanical machines. • ENIAC was named an IEEE Milestone in 1987. Gaint Brain • ENIAC contained 17,468 vacuum tubes, 7,200 crystal diodes, 1,500 relays, 70,000 resistors, 10,000 capacitors and around 5 million hand-soldered joints. It weighed more than 27 tons, took up 167 m2, and consumed 150 kW of power. • This led to the rumor that whenever the computer was switched on, lights in Philadelphia dimmed. • Input was from an IBM card reader, and an IBM card punch was used for output. Development of micro-computers modern PC 1981 IBM PC 5150 CPU: Intel i3,i5,i7, CPU: 8088, 5 MHz 3 GHz Floppy disk or cassette Solid state disk 1984 Macintosh Steve Jobs modern iMac Supercomputers The CDC (Control Data Corporation) 6600, released in 1964, is generally considered the first supercomputer. Seymour Roger Cray (1925-1996) The father of supercomputing, Cray-1 who created the supercomputer industry. Cray Inc. -
FCMSSR Meeting 2018-01 All Slides
Federal Committee for Meteorological Services and Supporting Research (FCMSSR) Dr. Neil Jacobs Assistant Secretary for Environmental Observation and Prediction and FCMSSR Chair April 30, 2018 Office of the Federal Coordinator for Meteorology Services and Supporting Research 1 Agenda 2:30 – Opening Remarks (Dr. Neil Jacobs, NOAA) 2:40 – Action Item Review (Dr. Bill Schulz, OFCM) 2:45 – Federal Coordinator's Update (OFCM) 3:00 – Implementing Section 402 of the Weather Research And Forecasting Innovation Act Of 2017 (OFCM) 3:20 – Federal Meteorological Services And Supporting Research Strategic Plan and Annual Report. (OFCM) 3:30 – Qualification Standards For Civilian Meteorologists. (Mr. Ralph Stoffler, USAF A3-W) 3:50 – National Earth System Predication Capability (ESPC) High Performance Computing Summary. (ESPC Staff) 4:10 – Open Discussion (All) 4:20 – Wrap-Up (Dr. Neil Jacobs, NOAA) Office of the Federal Coordinator for Meteorology Services and Supporting Research 2 FCMSSR Action Items AI # Text Office Comment Status Due Date Responsible 2017-2.1 Reconvene JAG/ICAWS to OFCM, • JAG/ICAWS convened. Working 04/30/18 develop options to broaden FCMSSR • Options presented to FCMSSR Chairmanship beyond Agencies ICMSSR the Undersecretary of Commerce • then FCMSSR with a for Oceans and Atmosphere. revised Charter Draft a modified FCMSSR • Draft Charter reviewed charter to include ICAWS duties by ICMSSR. as outlined in Section 402 of the • Pending FCMSSR and Weather Research and Forecasting OSTP approval to Innovation Act of 2017 and secure finalize Charter for ICMSSR concurrence. signature. Recommend new due date: 30 June 2018. 2017-2.2 Publish the Strategic Plan for OFCM 1/12/18: Plan published on Closed 11/03/17 Federal Weather Coordination as OFCM website presented during the 24 October 2017 FCMMSR Meeting. -
Jack Dongarra: Supercomputing Expert and Mathematical Software Specialist
Biographies Jack Dongarra: Supercomputing Expert and Mathematical Software Specialist Thomas Haigh University of Wisconsin Editor: Thomas Haigh Jack J. Dongarra was born in Chicago in 1950 to a he applied for an internship at nearby Argonne National family of Sicilian immigrants. He remembers himself as Laboratory as part of a program that rewarded a small an undistinguished student during his time at a local group of undergraduates with college credit. Dongarra Catholic elementary school, burdened by undiagnosed credits his success here, against strong competition from dyslexia.Onlylater,inhighschool,didhebeginto students attending far more prestigious schools, to Leff’s connect material from science classes with his love of friendship with Jim Pool who was then associate director taking machines apart and tinkering with them. Inspired of the Applied Mathematics Division at Argonne.2 by his science teacher, he attended Chicago State University and majored in mathematics, thinking that EISPACK this would combine well with education courses to equip Dongarra was supervised by Brian Smith, a young him for a high school teaching career. The first person in researcher whose primary concern at the time was the his family to go to college, he lived at home and worked lab’s EISPACK project. Although budget cuts forced Pool to in a pizza restaurant to cover the cost of his education.1 make substantial layoffs during his time as acting In 1972, during his senior year, a series of chance director of the Applied Mathematics Division in 1970– events reshaped Dongarra’s planned career. On the 1971, he had made a special effort to find funds to suggestion of Harvey Leff, one of his physics professors, protect the project and hire Smith. -
The Sunway Taihulight Supercomputer: System and Applications
SCIENCE CHINA Information Sciences . RESEARCH PAPER . July 2016, Vol. 59 072001:1–072001:16 doi: 10.1007/s11432-016-5588-7 The Sunway TaihuLight supercomputer: system and applications Haohuan FU1,3 , Junfeng LIAO1,2,3 , Jinzhe YANG2, Lanning WANG4 , Zhenya SONG6 , Xiaomeng HUANG1,3 , Chao YANG5, Wei XUE1,2,3 , Fangfang LIU5 , Fangli QIAO6 , Wei ZHAO6 , Xunqiang YIN6 , Chaofeng HOU7 , Chenglong ZHANG7, Wei GE7 , Jian ZHANG8, Yangang WANG8, Chunbo ZHOU8 & Guangwen YANG1,2,3* 1Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing 100084, China; 2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; 3National Supercomputing Center in Wuxi, Wuxi 214072, China; 4College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; 5Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; 6First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China; 7Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; 8Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China Received May 27, 2016; accepted June 11, 2016; published online June 21, 2016 Abstract The Sunway TaihuLight supercomputer is the world’s first system with a peak performance greater than 100 PFlops. In this paper, we provide a detailed introduction to the TaihuLight system. In contrast with other existing heterogeneous supercomputers, which include both CPU processors and PCIe-connected many-core accelerators (NVIDIA GPU or Intel Xeon Phi), the computing power of TaihuLight is provided by a homegrown many-core SW26010 CPU that includes both the management processing elements (MPEs) and computing processing elements (CPEs) in one chip. -
Germany's Top500 Businesses
GERMANY’S TOP500 DIGITAL BUSINESS MODELS IN SEARCH OF BUSINESS CONTENTS FOREWORD 3 INSIGHT 1 4 SLOW GROWTH RATES YET HIGH SALES INSIGHT 2 6 NOT ENOUGH REVENUE IS ATTRIBUTABLE TO DIGITIZATION INSIGHT 3 10 EU REGULATIONS ARE WEAKENING INNOVATION INSIGHT 4 12 THE GERMAN FEDERAL GOVERNMENT COULD TURN INTO AN INNOVATION DRIVER CONCLUSION 14 FOREWORD Large German companies are on the lookout. Their purpose: To find new growth prospects. While revenue increases of more than 5 percent on average have not been uncommon for Germany’s 500 largest companies in the past, that level of growth has not occurred for the last four years. The reasons are obvious. With their high export rates, This study is intended to examine critically the Germany’s industrial companies continue to be major opportunities arising at the beginning of a new era of players in the global market. Their exports have, in fact, technology. Accenture uses four insights to not only been so high in the past that it is now increasingly describe the progress that has been made in digital difficult to sustain their previous rates of growth. markets, but also suggests possible steps companies can take to overcome weak growth. Accenture regularly examines Germany’s largest companies on the basis of their ranking in “Germany’s Top500,” a list published every year in the German The four insights in detail: daily newspaper DIE WELT. These 500 most successful German companies generate revenue of more than INSIGHT 1 one billion Euros. In previous years, they were the Despite high levels of sales, growth among Germany’s engines of the German economy. -
BDEC2 Poznan Japanese HPC Infrastructure Update
BDEC2 Poznan Japanese HPC Infrastructure Update Presenter: Masaaki Kondo, Riken R-CCS/Univ. of Tokyo (on behalf of Satoshi Matsuoka, Director, Riken R-CCS) 1 Post-K: The Game Changer (2020) 1. Heritage of the K-Computer, HP in simulation via extensive Co-Design • High performance: up to x100 performance of K in real applications • Multitudes of Scientific Breakthroughs via Post-K application programs • Simultaneous high performance and ease-of-programming 2. New Technology Innovations of Post-K Global leadership not just in High Performance, esp. via high memory BW • the machine & apps, but as Performance boost by “factors” c.f. mainstream CPUs in many HPC & Society5.0 apps via BW & Vector acceleration cutting edge IT • Very Green e.g. extreme power efficiency Ultra Power efficient design & various power control knobs • Arm Global Ecosystem & SVE contribution Top CPU in ARM Ecosystem of 21 billion chips/year, SVE co- design and world’s first implementation by Fujitsu High Perf. on Society5.0 apps incl. AI • ARM: Massive ecosystem Architectural features for high perf on Society 5.0 apps from embedded to HPC based on Big Data, AI/ML, CAE/EDA, Blockchain security, etc. Technology not just limited to Post-K, but into societal IT infrastructures e.g. Clouds 2 Arm64fx & Post-K (to be renamed) Fujitsu-Riken design A64fx ARM v8.2 (SVE), 48/52 core CPU HPC Optimized: Extremely high package high memory BW (1TByte/s), on-die Tofu-D network BW (~400Gbps), high SVE FLOPS (~3Teraflops), various AI support (FP16, INT8, etc.) Gen purpose CPU – Linux,