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ACCELERATE RESEARCHNVIDIA TESLA
Lift the Barriers of HPC     Faster /              Maximum           Greater Budget &  More Research           Performance...
GPU Impact to Computational Research          More        Research                    +     Maximum                       ...
GPU Computing by Numbers          60                                           583   Universities                         ...
UCLADepartment of Physics and AstronomyChallenge   Accelerate Plasma Research with innovative Particle-in-Cell (PIC) Simul...
Add GPUs: Accelerate Science Applications       CPU                 GPU
207 GPU-Accelerated Applications              www.nvidia.com/appscatalog
3 Ways to Accelerate Applications                 Applications                 OpenACC               ProgrammingLibraries ...
GPU-Accelerated MATLAB Results 10x speedup in data clustering via K-   14x speedup in template matching routine      3x sp...
AMBER 12 - Extreme Performance with K20                                       DHRF JAC 23K Atoms (NVE)                    ...
NAMD 2.9                    Outstanding Strong Scaling with Multi-STMV                              Running NAMD version 2...
Try NVIDIA GPUs        Available Applications   Applications Catalog                                 www.nvidia.com/appsca...
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GPU Computing In Higher Education And Research

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Introduction to the revolutionary benefits GPU Computing offers to computational researchers in Higher Ed & Research.

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GPU Computing In Higher Education And Research

  1. 1. ACCELERATE RESEARCHNVIDIA TESLA
  2. 2. Lift the Barriers of HPC Faster / Maximum Greater Budget & More Research Performance Power EfficienciesFaster, More Discovery, More Performance More Performance Higher Accuracy per dollar per watt
  3. 3. GPU Impact to Computational Research More Research + Maximum Performance + Efficient Power88ns/day, 6x Faster 318% Higher Performance 2.5x Flops / Watt 54% Added Cost Tianhe-1A: CPU + GPU JAC simulation time 23,558 Atoms DHFR AMBER 11 Jaguar: CPU only CPU: Dual socket Intel XeonAxel Kohlmeyer: Temple University Tianhe-1A: #2 Top500; Jaguar: #3 Top500 X5670, 2.93 GHz (12 cores)
  4. 4. GPU Computing by Numbers 60 583 Universities Universities 150K 1.5MCUDA Downloads CUDA Downloads 4,000 22,500Academic Papers Academic Papers 1 52 Supercomputer Supercomputers 2008 2012
  5. 5. UCLADepartment of Physics and AstronomyChallenge Accelerate Plasma Research with innovative Particle-in-Cell (PIC) Simulations Overcome space and power constraints in data centers Integrate into shared computing strategy across institutes and centers at UCLASolution GPU cluster 96 server nodes 288 NVIDIA Tesla GPUs Upgraded GPUs to NVIDIA Tesla M2090s (from M2070)Impact Upgrades resulted in 20% higher performance with same power cost GPUs extended to new groups within department for greatly accelerated modeling Solves faster performance requirements within limited space and power constraints #235 on prestigious Top500 list with only 6 Racks
  6. 6. Add GPUs: Accelerate Science Applications CPU GPU
  7. 7. 207 GPU-Accelerated Applications www.nvidia.com/appscatalog
  8. 8. 3 Ways to Accelerate Applications Applications OpenACC ProgrammingLibraries Directives Languages “Drop-in” Easily Accelerate MaximumAcceleration Applications Flexibility THRUST C BLAS, LAPACK C++ FFT PGI Accelerator Fortran NPP CAPS HMPP OpenCL Sparse CRAY DirectCompute Imaging Java RNG Python
  9. 9. GPU-Accelerated MATLAB Results 10x speedup in data clustering via K- 14x speedup in template matching routine 3x speedup in estimating 7.6 million means clustering algorithm (part of cancer cell image analysis) contract prices using Black-Scholes model17x speedup in simulating the movement 4x speedup in adaptive filtering routine 4x speedup in wave equation solving (part of 3072 celestial objects (part of acoustic tracking algorithm) of seismic data processing algorithm)
  10. 10. AMBER 12 - Extreme Performance with K20 DHRF JAC 23K Atoms (NVE) Running AMBER 12 GPU Support Revision 12.1 SPFP with CUDA 4.2.9 ECC Off 120 The blue node contains 2x Intel E5-2687W CPUs 95.59 (8 Cores per CPU) 100 Each green node contains 2x Intel E5-2687W CPUs (8 Cores per CPU) plus 2x NVIDIA K20 GPUNanoseconds / Day 80 60 40 20 12.47 0 1 Node 1 Node DHFR Gain > 7.5X throughput/performance by adding just 2 K20 GPUs when compared to dual CPU performance
  11. 11. NAMD 2.9 Outstanding Strong Scaling with Multi-STMV Running NAMD version 2.9 Each blue XE6 CPU node contains 1x AMD 100 STMV on Hundreds of Nodes 1600 Opteron (16 Cores per CPU). 1.2 Fermi XK6 Each green XK6 CPU+GPU node contains 1x AMD 1600 Opteron (16 Cores per CPU) 1 and an additional 1x NVIDIA X2090 GPU. CPU XK6 2.7xNanoseconds / Day 0.8 2.9x 0.6 0.4 0.2 3.6x 3.8x Concatenation of 100 0 Satellite Tobacco Mosaic Virus 32 64 128 256 512 640 768 # of Nodes Accelerate your science by 2.7-3.8x when compared to CPU-based supercomputers
  12. 12. Try NVIDIA GPUs Available Applications Applications Catalog www.nvidia.com/appscatalogQuick Application Acceleration OpenACC Directives www.nvidia.com/gpudirectives Easy & Free GPU Test Drive GPU Test Drive Cluster www.nvidia.com/gputestdrive
  13. 13. THANK YOU

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