Your SlideShare is downloading. ×
N A G P A R I S280101
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

N A G P A R I S280101


Published on

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.

Published in: Technology
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 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