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N A G P A R I S280101

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The presentation will introduce Nvidia and the concept of GPU computing in the context of Financial Services industry. Customer successes are referenced where dramatic speed-ups in performance have …

The presentation will introduce Nvidia and the concept of GPU computing in the context of Financial Services industry. Customer successes are referenced where dramatic speed-ups in performance have been achieved.

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  • 1. GPU OVERVIEW IN FINANCIAL SERVICES ALASTAIR HOUSTON COMPUTE FSI SALES MANAGER
  • 2. Agenda Nvidia and HPC markets GPU Overview CUDA and OpenCL Current FS deployments © NVIDIA Corporation 2009
  • 3. CUDA Runs on NVIDIA GPUs … Over 80 Million CUDA GPUs Deployed GeForce® TeslaTM Quadro® Entertainment High-Performance Computing Design & Creation © NVIDIA Corporation 2009
  • 4. 146X 36X 18X 50X 100X Medical Imaging Molecular Dynamics Video Transcoding Matlab Computing Astrophysics U of Utah U of Illinois, Urbana Elemental Tech AccelerEyes RIKEN 50x – 150x 149X 47X 20X 130X 30X Financial simulation Linear Algebra 3D Ultrasound Quantum Chemistry Gene Sequencing Oxford Universidad Jaime Techniscan U of Illinois, Urbana U of Maryland © NVIDIA Corporation 2009
  • 5. Options Pricing, Risk Modeling, Algorithmic Trading Options pricing use Monte Carlo (MC) simulations Random Number Generators (RNG) are key to MC Up to 100x speed-up in RNGs using CUDA 25-60x overall speedup in Monte Carlo simulations © NVIDIA Corporation 2009
  • 6. Co-Processing CPU GPU The Right Processor for the Right Tasks © NVIDIA Corporation 2009
  • 7. The Performance Gap Widens Further 8x double precision ECC L1, L2 Caches 1 TF Single Precision 4GB Memory NVIDIA GPU © NVIDIA Corporation 2009 X86 CPU
  • 8. Introducing the ‘Fermi’ Architecture The Soul of a Supercomputer in the body of a GPU 3 billion transistors DRAM I/F DRAM I/F DRAM I/F Over 2× the cores (512 total) 8× the peak DP performance DRAM I/F DRAM I/F HOST I/F ECC L2 L1 and L2 caches Giga Thread DRAM I/F DRAM I/F ~2× memory bandwidth (GDDR5) Up to 1 Terabyte of GPU memory DRAM I/F DRAM I/F DRAM I/F Concurrent kernels Hardware support for C++ © NVIDIA Corporation 2009
  • 9. NVIDIA Compute Products Board Level Products 1U Server Product 1 Tesla GPU 4 Tesla GPUs Workstation Product Data Center Product OEM Product © NVIDIA Corporation 2009
  • 10. CUDA C and OpenCL Momentum Over 100,000,000 installed CUDA- Architecture GPUs GPU Computing Applications Over 60,000 GPU Computing Developers (1/09) Windows, Linux and MacOS Platforms C OpenCL DirectX FORTRAN Python, supported Compute Java, … With CUDA Extensions Over 60,000 developers 1st GPU demo Microsoft’s GPU Microsoft’ SW supplied by: Compute Kernels GPU Computing spans Shipped 1st OpenCL Computing API • The Portland Group Driver API Bindings Consumer applications Running in Production Driver Supports all CUDA- CUDA- • NCSA release since 2008 to HPC Strategic developers Architecture GPUs SDK + Lib’s + Visual Lib’ since G80 (DX10 and using NV SW today Profiler and Debugger future DX11 GPUs) 200+ Universities teaching the CUDA Architecture and GPU Computing NVIDIA GPU with the CUDA Parallel Computing Architecture © NVIDIA Corporation 2009
  • 11. NVIDIA Nexus Nexus is a GPU application development suite that integrates directly into Visual Studio. A C/CUDA source debugger for both the CUDA runtime and driver API New C/CUDA performance analysis/trace tools © NVIDIA Corporation 2009
  • 12. FSI CUSTOMER DEPLOYMENTS © NVIDIA Corporation 2009
  • 13. Case Study: Equity Derivatives 15 15x Faster 1 2 Tesla S1070 16x Less Space 500 CPU Cores $24 K 10x Lower Cost $250 K 2.8 KWatts 13x Lower Power 37.5 KWatts Source: BNP Paribas, March 4, 2009 © NVIDIA Corporation 2009
  • 14. Case Study: Security Pricing 2 hours 8x Faster 16 hours 48 Tesla S1070 10x Less Space 8000 CPU Cores Source: Wall Street & Technology, September 24, 2009 © NVIDIA Corporation 2009

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