Opportunities for HPC in Industry. Stan Posey HPC Applications and Industry Development NVIDIA, Santa Clara, CA, USA
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1 GPU Computing Opportunities for HPC in Industry Stan Posey HPC Applications and Industry Development NVIDIA, Santa Clara, CA, USA
2 Why GPU Computing? High Performance
3 Objective of GPU Computing: HPC Efficiency Operations Should be Processed on the Device Best Suited for HPC Efficiency [Efficiency in Performance, Parallel Scaling, Power Consumption, Space, Human Effort, etc.] CPU + GPU Co-Processing Heterogeneous Computing
4 GPU Computing for Desktops and Servers Tesla S1070 1U System Tesla C1060 Computing Board GPUs 4 Tesla GPUs 1 Tesla GPU Single Precision Performance Double Precision Performance 4.14 Teraflops 933 Gigaflops 346 Gigaflops 78 Gigaflops Memory 16 GB (4 GB / GPU) 4 GB
5 NVIDIA Accomplishments in Recent 5 Years 2004 Began strategic investments in GPU as an HPC microprocessor 2006 G80 first GPU with built-in compute features, 128 core multithreaded, scalable architecture CUDA SDK Beta 2007 Tesla 8-series introduction based on G80, 128 cores gen-1 CUDA SDK 1.0, Tesla 10-series introduction based on GT 200, 240 cores gen-2 CUDA SDK (2010 release) Tesla third generation, code named Fermi 512 cores gen-3 CUDA SDK 3.0
6 NVIDIA GPU Progression for HPC Market Research Adoption Tesla gen-1; 2007 GPU achievements demonstrated at conferences; in journals Research Expansion Industry Adoption Tesla gen-2; 2008 Broad research expansion to all disciplines of science Moderate ISV development and product introduction In-house industry software: seismic; finance; aerospace Gov t funding of GPU developments for target industries Industry Expansion Tesla gen-3; 2010 (Fermi) Broad ISV development move ongoing in preparation for Fermi Benefits to pharmaceuticals, manufacturing, energy, etc.
7 Abundance of HPC Research Achievements Example: Gene Sequencing and Assembly in Bioinformatics
8 Select Industry HPC Customers and ISVs Life Sciences & Medical Equipment Oil & Gas EDA Finance CAE Max Planck FDA Robarts Research Medtronic AGC Evolved machines Smith-Waterman DNA sequencing AutoDock NAMD/VMD Howard Hughes Medical CRIBI Genomics GE Healthcare Siemens Techniscan Boston Scientific Eli Lilly Silicon Informatics Stockholm Research Harvard Delaware Pittsburg ETH Zurich Institute Atomic Physics Hess TOTAL CGG/Veritas Chevron Headwave Acceleware Seismic City P-Wave Seismic Imaging Mercury Computer ffa Geostar Synopsys Nascentric Gauda CST Agilent Symcor Level 3 SciComp Hanweck Quant Catalyst RogueWav e BNP Paribas AccelerEyes MathWorks Wolfram National Instruments Ansys Access Analytics Tech-x RIKEN SOFA Renault Boeing
9 Tesla Results for Risk Analysis in Finance Exegy Ticker Plant for Accelerated Risk Evaluation Valuation at Risk (VaR) 1024 equities, 2 20 simulations 180x Faster with CUDA 45 mins 12 secs
10 Tesla Results for Seismic in Oil & Gas CPU Rack: 64 CPU nodes (DS = 512 CPU cores) GPU Rack: 8 Tesla S1070s (32 GPUs) +16 CPU nodes Source: TOTAL, ISC Intel Xeon GHz 4 Tesla GPUs (1 Tesla S1070) Source: Chevron, SEG 2008
11 Tesla Results for Seismic in Oil & Gas Successful Customers GPU vs CPU Improvements Oil & Gas ISVs Performance / Watt 18x - 27x 12x - 17x Performance / Space 20x - 31x 15x - 20x Performance / Cost 15x - 20x 10x - 12x
12 HPC Focus Areas for Research and Industry NVIDIA HPC Focus Area Sample Applications Research HPC CPM [Computational Physics and Mathematics] ALEGRA; VPIC; Neutronics CCB [Computational Biology and Chemistry] GAMESS; AMBER; BLAST CWO [Climate Weather Ocean modeling] WRF; HYCOM; MOM; HOMME Industry HPC EEP [Energy Exploration and Production] GeoDepth; Eclipse; ProMAX CAE [Computer Aided Engineering] FLUENT; STAR-CD; Abaqus EDA [Electronic Design Automation] Proteus; OPCpro; Hercules CFE [Computational Finance and Economics] Monte Carlo Simulations DIR [Digital Imaging and Rendering] RenderMan, etc
13 Drivers for Research HPC Objectives National Interests of Global Advancement and Improved Quality of Life Competition Economic Growth Technology Export Education Development Global Advancement Defense Energy Technology Transfer to Industry RESEARCH Quality of Life Improvements Health Sciences Environment / Climate Agriculture Science Application Software Objectives: Efficient Compilers; Development Tools
14 Drivers for Industry HPC Objectives Product Development for Regulated and Competitive Consumer Markets Competition Reduced Lead Times Reduced Cost Improved Quality Consumer Appeal Price-Performance Latest Features Comfort & Style INDUSTRY Government Regulations Product Safety Energy Efficient Environment Impact Application Software Objectives: Efficient ISV and COTS software
15 Example: Ford Motor and CAE ISV Software NOTE: This list of applications is not meant to imply use of NVIDIA GPU computing
16 GPU Computing Opportunities in Automotive Improved Crashworthiness Simulation Through Optimization
17 GPU Computing Opportunities in Automotive Improved Crashworthiness Simulation Through Optimization
18 GPU Computing Opportunities in Automotive Improved Crashworthiness Simulation Through Optimization
19 NVIDIA GPU Progression for HPC Market Research Adoption Tesla gen-1; 2007 GPU achievements demonstrated at conferences; in journals Research Expansion Industry Adoption Tesla gen-2; 2008 Broad research expansion to all disciplines of science Moderate ISV development and product introduction In-house industry software: seismic; finance; aerospace Gov t funding of GPU developments for target industries Industry Expansion Tesla gen-3; 2010 (Fermi) Broad ISV development move ongoing in preparation for Fermi Benefits to pharmaceuticals, manufacturing, energy, etc.
20 Introduction of the Fermi Architecture The World s 1 st Complete GPU Computing Architecture More than 2x the cores (512 total) 8x more peak double precision performance; > 30% single precision ECC for all levels of memory L1 (64K) and unified L2 (768K) cache memory hierarchy 1 st ever for GPU ~2x more memory bandwidth (Use of GDDR5 memory vs. GDDR3) Roadmap for 1 TB of main memory Concurrent Kernels, C++, FTN
21 CUDA Parallel Computing Architecture OpenCL is trademark of Apple Inc. used under license to the Khronos Group Inc.
22 CUDA GPU Computing Global Ecosystem 200+ Universities Teach CUDA UIUC MIT Harvard Berkeley Cambridge Oxford Applications Oil & Gas Finance CFD Medical Biophysics Imaging Numerics DSP EDA IIT Delhi Tsinghua Dortmundt ETH Zurich Moscow NTU Libraries FFT BLAS LAPACK Image processing Video processing Signal processing Vision Languages Compilers C, C++ DirectX Fortran Java OpenCL Python PGI Fortran CAPs HMPP MCUDA MPI NOAA Fortran2C OpenMP Consultants ANEO GPU Tech OEMs
23 Increasing GPU Performance Gap With CPUs Tesla 10-Series (Today) Fermi (Q2/2010)
24 ORNL Planning for Multi-Pflops Fermi System Oak Ridge National Lab (ORNL) has already announced it will be using Fermi technology in an upcoming super that is expected to be 10-times more powerful than today s fastest supercomputer. Since ORNL s Jaguar supercomputer, for all intents and purposes, holds that title, and is in the process of being upgraded to 2.3 Petaflops September
25 Recent NVIDIA Developments in HPC Tesla Fermi Announcement and Information NVIDIA Collaboration with Microsoft in HPC NVIDIA Support Announced for OpenCL ORNL and NVIDIA to Collaborate on 20 PFLOPS
26 Thank you, Questions? Stan Posey HPC Applications and Industry Development NVIDIA, Santa Clara, CA, USA
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