SX-Aurora TSUBASA Introduction Vector Supercomputer Technology on a Pcie Card What Is Vector Processor? (1/2)
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User's Manual
Management Number:OUCMC-super-技術-007-06 National University Corporation Osaka University Cybermedia Center User's manual NEC First Government & Public Solutions Division Summary 1. System Overview ................................................................................................. 1 1.1. Overall System View ....................................................................................... 1 1.2. Thress types of Computiong Environments ........................................................ 2 1.2.1. General-purpose CPU environment .............................................................. 2 1.2.2. Vector computing environment ................................................................... 3 1.2.3. GPGPU computing environment .................................................................. 5 1.3. Three types of Storage Areas ........................................................................... 6 1.3.1. Features.................................................................................................. 6 1.3.2. Various Data Access Methods ..................................................................... 6 1.4. Three types of Front-end nodes ....................................................................... 7 1.4.1. Features.................................................................................................. 7 1.4.2. HPC Front-end ......................................................................................... 7 1.4.1. HPDA Front-end ...................................................................................... -
Petaflops for the People
PETAFLOPS SPOTLIGHT: NERSC housands of researchers have used facilities of the Advanced T Scientific Computing Research (ASCR) program and its EXTREME-WEATHER Department of Energy (DOE) computing predecessors over the past four decades. Their studies of hurricanes, earthquakes, NUMBER-CRUNCHING green-energy technologies and many other basic and applied Certain problems lend themselves to solution by science problems have, in turn, benefited millions of people. computers. Take hurricanes, for instance: They’re They owe it mainly to the capacity provided by the National too big, too dangerous and perhaps too expensive Energy Research Scientific Computing Center (NERSC), the Oak to understand fully without a supercomputer. Ridge Leadership Computing Facility (OLCF) and the Argonne Leadership Computing Facility (ALCF). Using decades of global climate data in a grid comprised of 25-kilometer squares, researchers in These ASCR installations have helped train the advanced Berkeley Lab’s Computational Research Division scientific workforce of the future. Postdoctoral scientists, captured the formation of hurricanes and typhoons graduate students and early-career researchers have worked and the extreme waves that they generate. Those there, learning to configure the world’s most sophisticated same models, when run at resolutions of about supercomputers for their own various and wide-ranging projects. 100 kilometers, missed the tropical cyclones and Cutting-edge supercomputing, once the purview of a small resulting waves, up to 30 meters high. group of experts, has trickled down to the benefit of thousands of investigators in the broader scientific community. Their findings, published inGeophysical Research Letters, demonstrated the importance of running Today, NERSC, at Lawrence Berkeley National Laboratory; climate models at higher resolution. -
DMI-HIRLAM on the NEC SX-6
DMI-HIRLAM on the NEC SX-6 Maryanne Kmit Meteorological Research Division Danish Meteorological Institute Lyngbyvej 100 DK-2100 Copenhagen Ø Denmark 11th Workshop on the Use of High Performance Computing in Meteorology 25-29 October 2004 Outline • Danish Meteorological Institute (DMI) • Applications run on NEC SX-6 cluster • The NEC SX-6 cluster and access to it • DMI-HIRLAM - geographic areas, versions, and improvements • Strategy for utilization and operation of the system DMI - the Danish Meteorological Institute DMI’s mission: • Making observations • Communicating them to the general public • Developing scientific meteorology DMI’s responsibilities: • Serving the meteorological needs of the kingdom of Denmark • Denmark, the Faroes and Greenland, including territorial waters and airspace • Predicting and monitoring weather, climate and environmental conditions, on land and at sea Applications running on the NEC SX-6 cluster Operational usage: • Long DMI-HIRLAM forecasts 4 times a day • Wave model forecasts for the North Atlantic, the Danish waters, and for the Mediterranean Sea 4 times a day • Trajectory particle model and ozone forecasts for air quality Research usage: • Global climate simulations • Regional climate simulations • Research and development of operational and climate codes Cluster interconnect GE−int Completely closed Connection to GE−ext Switch internal network institute network Switch GE GE nec1 nec2 nec3 nec4 nec5 nec6 nec7 nec8 GE GE neci1 neci2 FC 4*1Gbit/s FC links per node azusa1 asama1 FC McData FC 4*1Gbit/s FC links per azusa fiber channel switch 8*1Gbit/s FC links per asama azusa2 asama2 FC 2*1Gbit/s FC links per lun Disk#0 Disk#1 Disk#2 Disk#3 Cluster specifications • NEC SX-6 (nec[12345678]) : 64M8 (8 vector nodes with 8 CPU each) – Desc. -
Performance Modeling the Earth Simulator and ASCI Q
PAL CCS-3 Performance Modeling the Earth Simulator and ASCI Q Darren J. Kerbyson Adolfy Hoisie, Harvey J. Wasserman Performance and Architectures Laboratory (PAL) Los Alamos National Laboratory April 2003 Los Alamos PAL CCS-3 “26.58Tflops on AFES … 64.9% of peak (640nodes)” “14.9Tflops on Impact-3D …. 45% of peak (512nodes)” “10.5Tflops on PFES … 44% of peak (376nodes)” 40Tflops 20Tflops Los Alamos PAL Talk Overview CCS-3 G Overview of the Earth Simulator – A (quick) view of the architecture of the Earth Simulator (and Q) – A look at its performance characteristics G Application Centric Performance Models – Method of comparing performance is to use trusted models of applications that we are interested in, e.g. SAGE and Sweep3D. – Analytical / Parameterized in system & application characteristics G Models can be used to provide: – Predicted performance prior to availability (hardware or software) – Insight into performance – Performance Comparison (which is better?) G System Performance Comparison (Earth Simulator vs ASCI Q) Los Alamos PAL Earth Simulator: Overview CCS-3 ... ... RCU RCU . RCU AP AP AP Memory Memory Memory 0-7 0-7 0-7 ... Node0 Node1 Node639 640x640 ... crossbar G 640 Nodes (Vector Processors) G interconnected by a single stage cross-bar – Copper interconnect (~3,000Km wire) G NEC markets a product – SX-6 – Not the same as an Earth Simulator Node - similar but different memory sub-system Los Alamos PAL Earth Simulator Node CCS-3 Crossbar LAN network Disks AP AP AP AP AP AP AP AP 0 1 2 3 4 5 6 7 RCU IOP Node contains: 8 vector processors (AP) 16GByte memory Remote Control Unit (RCU) I/O Processor (IOP) 2 3 4 5 6 0 1 31 M M M M M M M . -
Project Aurora 2017 Vector Inheritance
Vector Coding a simple user’s perspective Rudolf Fischer NEC Deutschland GmbH Düsseldorf, Germany SIMD vs. vector Input Pipeline Result Scalar SIMD people call it “vector”! Vector SX 2 © NEC Corporation 2017 Data Parallelism ▌‘Vector Loop’, data parallel do i = 1, n Real, dimension(n): a,b,c a(i) = b(i) + c(i) … end do a = b + c ▌‘Scalar Loop’, not data parallel, ex. linear recursion do i = 2, n a(i) = a(i-1) + b(i) end do ▌Reduction? do i = 1, n, VL do i_ = i, min(n,i+VL-1) do i = 1, n s_(i_) = s_(i_) + v(i_)* w(i_) s = s + v(i)* w(i) end do end do end do s = reduction(s_) (hardware!) 3 © NEC Corporation 2017 Vector Coding Paradigm ▌‘Scalar’ thinking / coding: There is a (grid-point,particle,equation,element), what am I going to do with it? ▌‘Vector’ thinking / coding: There is a certain action or operation, to which (grid-points,particles,equations,elements) am I going to apply it simultaneously? 4 © NEC Corporation 2017 Identifying the data-parallel structure ▌ Very simple case: do j = 2, m-1 do i = 2, n-1 rho_new(i,j) = rho_old(i,j) + dt * something_with_fluxes(i+/-1,j+/-1) end do end do ▌ Does it tell us something? Partial differential equations Local theories Vectorization is very “natural” 5 © NEC Corporation 2017 Identifying the data-parallel subset ▌ Simple case: V------> do j = 1, m |+-----> do i = 2, n || a(i,j) = a(i-1,j) + b(i,j) |+----- end do V------ end do ▌ The compiler will vectorise along j ▌ non-unit-stride access, suboptimal ▌ Results for a certain case: Totally scalar (directives): 25.516ms Avoid outer vector loop: -
2020 Global High-Performance Computing Product Leadership Award
2020 GLOBAL HIGH-PERFORMANCE COMPUTING PRODUCT LEADERSHIP AWARD Strategic Imperatives Frost & Sullivan identifies three key strategic imperatives that impact the automation industry: internal challenges, disruptive technologies, and innovative business models. Every company that is competing in the automation space is obligated to address these imperatives proactively; failing to do so will almost certainly lead to stagnation or decline. Successful companies overcome the challenges posed by these imperatives and leverage them to drive innovation and growth. Frost & Sullivan’s recognition of NEC is a reflection of how well it is performing against the backdrop of these imperatives. Best Practices Criteria for World-Class Performance Frost & Sullivan applies a rigorous analytical process to evaluate multiple nominees for each award category before determining the final award recipient. The process involves a detailed evaluation of best practices criteria across two dimensions for each nominated company. NEC excels in many of the criteria in the high-performance computing (HPC) space. About NEC Established in 1899, NEC is a global IT, network, and infrastructure solution provider with a comprehensive product portfolio across computing, data storage, embedded systems, integrated IT infrastructure, network products, software, and unified communications. Headquartered in Tokyo, Japan, NEC has been at the forefront of accelerating the industrial revolution of the 20th and 21st © Frost & Sullivan 2021 The Growth Pipeline Company™ centuries by leveraging its technical knowhow and product expertise across thirteen different industries1 in industrial and energy markets. Deeply committed to the vision of orchestrating a better world. NEC envisions a future that embodies the values of safety, security, fairness, and efficiency, thus creating long-lasting social value. -
Hardware Technology of the Earth Simulator 1
Special Issue on High Performance Computing 27 Architecture and Hardware for HPC 1 Hardware Technology of the Earth Simulator 1 By Jun INASAKA,* Rikikazu IKEDA,* Kazuhiko UMEZAWA,* 5 Ko YOSHIKAWA,* Shitaka YAMADA† and Shigemune KITAWAKI‡ 5 1-1 This paper describes the hardware technologies of the supercomputer system “The Earth Simula- 1-2 ABSTRACT tor,” which has the highest performance in the world and was developed by ESRDC (the Earth 1-3 & 2-1 Simulator Research and Development Center)/NEC. The Earth Simulator has adopted NEC’s leading edge 2-2 10 technologies such as most advanced device technology and process technology to develop a high-speed and high-10 2-3 & 3-1 integrated LSI resulting in a one-chip vector processor. By combining this LSI technology with various advanced hardware technologies including high-density packaging, high-efficiency cooling technology against high heat dissipation and high-density cabling technology for the internal node shared memory system, ESRDC/ NEC has been successful in implementing the supercomputer system with its Linpack benchmark performance 15 of 35.86TFLOPS, which is the world’s highest performance (http://www.top500.org/ : the 20th TOP500 list of the15 world’s fastest supercomputers). KEYWORDS The Earth Simulator, Supercomputer, CMOS, LSI, Memory, Packaging, Build-up Printed Wiring Board (PWB), Connector, Cooling, Cable, Power supply 20 20 1. INTRODUCTION (Main Memory Unit) package are all interconnected with the fine coaxial cables to minimize the distances The Earth Simulator has adopted NEC’s most ad- between them and maximize the system packaging vanced CMOS technologies to integrate vector and density corresponding to the high-performance sys- 25 25 parallel processing functions for realizing a super- tem. -
Shared-Memory Vector Systems Compared
Shared-Memory Vector Systems Compared Robert Bell, CSIRO and Guy Robinson, Arctic Region Supercomputing Center ABSTRACT: The NEC SX-5 and the Cray SV1 are the only shared-memory vector computers currently being marketed. This compares with at least five models a few years ago (J90, T90, SX-4, Fujitsu and Hitachi), with IBM, Digital, Convex, CDC and others having fallen by the wayside in the early 1990s. In this presentation, some comparisons will be made between the architecture of the survivors, and some performance comparisons will be given on benchmark and applications codes, and in areas not usually presented in comparisons, e.g. file systems, network performance, gzip speeds, compilation speeds, scalability and tools and libraries. KEYWORDS: SX-5, SV1, shared-memory, vector systems averaged 95% on the larger node, and 92% on the smaller 1. Introduction node. HPCCC The SXes are supported by data storage systems – The Bureau of Meteorology and CSIRO in Australia SAM-FS for the Bureau, and DMF on a Cray J90se for established the High Performance Computing and CSIRO. Communications Centre (HPCCC) in 1997, to provide ARSC larger computational facilities than either party could The mission of the Arctic Region Supercomputing acquire separately. The main initial system was an NEC Centre is to support high performance computational SX-4, which has now been replaced by two NEC SX-5s. research in science and engineering with an emphasis on The current system is a dual-node SX-5/24M with 224 high latitudes and the Arctic. ARSC provides high Gbyte of memory, and this will become an SX-5/32M performance computational, visualisation and data storage with 224 Gbyte in July 2001. -
Recent Supercomputing Development in Japan
Supercomputing in Japan Yoshio Oyanagi Dean, Faculty of Information Science Kogakuin University 2006/4/24 1 Generations • Primordial Ages (1970’s) – Cray-1, 75APU, IAP • 1st Generation (1H of 1980’s) – Cyber205, XMP, S810, VP200, SX-2 • 2nd Generation (2H of 1980’s) – YMP, ETA-10, S820, VP2600, SX-3, nCUBE, CM-1 • 3rd Generation (1H of 1990’s) – C90, T3D, Cray-3, S3800, VPP500, SX-4, SP-1/2, CM-5, KSR2 (HPC ventures went out) • 4th Generation (2H of 1990’s) – T90, T3E, SV1, SP-3, Starfire, VPP300/700/5000, SX-5, SR2201/8000, ASCI(Red, Blue) • 5th Generation (1H of 2000’s) – ASCI,TeraGrid,BlueGene/L,X1, Origin,Power4/5, ES, SX- 6/7/8, PP HPC2500, SR11000, …. 2006/4/24 2 Primordial Ages (1970’s) 1974 DAP, BSP and HEP started 1975 ILLIAC IV becomes operational 1976 Cray-1 delivered to LANL 80MHz, 160MF 1976 FPS AP-120B delivered 1977 FACOM230-75 APU 22MF 1978 HITAC M-180 IAP 1978 PAX project started (Hoshino and Kawai) 1979 HEP operational as a single processor 1979 HITAC M-200H IAP 48MF 1982 NEC ACOS-1000 IAP 28MF 1982 HITAC M280H IAP 67MF 2006/4/24 3 Characteristics of Japanese SC’s 1. Manufactured by main-frame vendors with semiconductor facilities (not ventures) 2. Vector processors are attached to mainframes 3. HITAC IAP a) memory-to-memory b) summation, inner product and 1st order recurrence can be vectorized c) vectorization of loops with IF’s (M280) 4. No high performance parallel machines 2006/4/24 4 1st Generation (1H of 1980’s) 1981 FPS-164 (64 bits) 1981 CDC Cyber 205 400MF 1982 Cray XMP-2 Steve Chen 630MF 1982 Cosmic Cube in Caltech, Alliant FX/8 delivered, HEP installed 1983 HITAC S-810/20 630MF 1983 FACOM VP-200 570MF 1983 Encore, Sequent and TMC founded, ETA span off from CDC 2006/4/24 5 1st Generation (1H of 1980’s) (continued) 1984 Multiflow founded 1984 Cray XMP-4 1260MF 1984 PAX-64J completed (Tsukuba) 1985 NEC SX-2 1300MF 1985 FPS-264 1985 Convex C1 1985 Cray-2 1952MF 1985 Intel iPSC/1, T414, NCUBE/1, Stellar, Ardent… 1985 FACOM VP-400 1140MF 1986 CM-1 shipped, FPS T-series (max 1TF!!) 2006/4/24 6 Characteristics of Japanese SC in the 1st G. -
Ushering in a New Era: Argonne National Laboratory & Aurora
Ushering in a New Era Argonne National Laboratory’s Aurora System April 2015 ANL Selects Intel for World’s Biggest Supercomputer 2-system CORAL award extends IA leadership in extreme scale HPC Aurora Argonne National Laboratory >180PF Trinity NNSA† April ‘15 Cori >40PF NERSC‡ >30PF July ’14 + Theta Argonne National Laboratory April ’14 >8.5PF >$200M ‡ Cray* XC* Series at National Energy Research Scientific Computing Center (NERSC). † Cray XC Series at National Nuclear Security Administration (NNSA). 2 The Most Advanced Supercomputer Ever Built An Intel-led collaboration with ANL and Cray to accelerate discovery & innovation >180 PFLOPS (option to increase up to 450 PF) 18X higher performance† >50,000 nodes Prime Contractor 13MW >6X more energy efficient† 2018 delivery Subcontractor Source: Argonne National Laboratory and Intel. †Comparison of theoretical peak double precision FLOPS and power consumption to ANL’s largest current system, MIRA (10PFs and 4.8MW) 3 Aurora | Science From Day One! Extreme performance for a broad range of compute and data-centric workloads Transportation Biological Science Renewable Energy Training Argonne Training Program on Extreme- Scale Computing Aerodynamics Biofuels / Disease Control Wind Turbine Design / Placement Materials Science Computer Science Public Access Focus Areas Focus US Industry and International Co-array Fortran Batteries / Solar Panels New Programming Models 4 Aurora | Built on a Powerful Foundation Breakthrough technologies that deliver massive benefits Compute Interconnect File System 3rd Generation 2nd Generation Intel® Xeon Phi™ Intel® Omni-Path Intel® Lustre* Architecture Software >17X performance† >20X faster† >3X faster† FLOPS per node >500 TB/s bi-section bandwidth >1 TB/s file system throughput >12X memory bandwidth† >2.5 PB/s aggregate node link >5X capacity† bandwidth >30PB/s aggregate >150TB file system capacity in-package memory bandwidth Integrated Intel® Omni-Path Architecture Processor code name: Knights Hill Source: Argonne National Laboratory and Intel. -
Architectural Trade-Offs in a Latency Tolerant Gallium Arsenide Microprocessor
Architectural Trade-offs in a Latency Tolerant Gallium Arsenide Microprocessor by Michael D. Upton A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical Engineering) in The University of Michigan 1996 Doctoral Committee: Associate Professor Richard B. Brown, CoChairperson Professor Trevor N. Mudge, CoChairperson Associate Professor Myron Campbell Professor Edward S. Davidson Professor Yale N. Patt © Michael D. Upton 1996 All Rights Reserved DEDICATION To Kelly, Without whose support this work may not have been started, would not have been enjoyed, and could not have been completed. Thank you for your continual support and encouragement. ii ACKNOWLEDGEMENTS Many people, both at Michigan and elsewhere, were instrumental in the completion of this work. I would like to thank my co-chairs, Richard Brown and Trevor Mudge, first for attracting me to Michigan, and then for allowing our group the freedom to explore many different ideas in architecture and circuit design. Their guidance and motivation combined to make this a truly memorable experience. I am also grateful to each of my other dissertation committee members: Ed Davidson, Yale Patt, and Myron Campbell. The support and encouragement of the other faculty on the project, Karem Sakallah and Ron Lomax, is also gratefully acknowledged. My friends and former colleagues Mark Rossman, Steve Sugiyama, Ray Farbarik, Tom Rossman and Kendall Russell were always willing to lend their assistance. Richard Oettel continually reminded me of the valuable support of friends and family, and the importance of having fun in your work. Our corporate sponsors: Cascade Design Automation, Chronologic, Cadence, and Metasoft, provided software and support that made this work possible. -
TOP500 Supercomputer Sites
7/24/2018 News | TOP500 Supercomputer Sites HOME | SEARCH | REGISTER RSS | MY ACCOUNT | EMBED RSS | SUPER RSS | Contact Us | News | TOP500 Supercomputer Sites http://top500.org/blog/category/feature-article/feeds/rss Are you the publisher? Claim or contact Browsing the Latest Browse All Articles (217 Live us about this channel Snapshot Articles) Browser Embed this Channel Description: content in your HTML TOP500 News Search Report adult content: 04/27/18--03:14: UK Commits a 0 0 Billion Pounds to AI Development click to rate The British government and the private sector are investing close to £1 billion Account: (login) pounds to boost the country’s artificial intelligence sector. The investment, which was announced on Thursday, is part of a wide-ranging strategy to make the UK a global leader in AI and big data. More Channels Under the investment, known as the “AI Sector Deal,” government, industry, and academia will contribute £603 million in new funding, adding to the £342 million already allocated in existing budgets. That brings the grand total to Showcase £945 million, or about $1.3 billion at the current exchange rate. The UK RSS Channel Showcase 1586818 government is also looking to increase R&D spending across all disciplines by 2.4 percent, while also raising the R&D tax credit from 11 to 12 percent. This is RSS Channel Showcase 2022206 part of a broader commitment to raise government spending in this area from RSS Channel Showcase 8083573 around £9.5 billion in 2016 to £12.5 billion in 2021. RSS Channel Showcase 1992889 The UK government policy paper that describes the sector deal meanders quite a bit, describing a lot of programs and initiatives that intersect with the AI investments, but are otherwise free-standing.