Accelerating Scientific Discovery V1

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We have made significant progress over the past couple of years working with scientists around the world helping them to accelerate scientific discovery - using Nvidia Tesla GPU and CUDA computing

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Accelerating Scientific Discovery V1

  1. 1. Accelerating Scientific Discovery using GPU Clusters http://www.nvidia.com/tesla
  2. 2. World’s Fastest Molecular Dynamics Simulation Sustained Performance of 1.87 Petaflops/s Institute of Process Engineering (IPE) Chinese Academy of Sciences (CAS) Simulaon  for  Crystalline  Silicon   Used  all  7168  Tesla  GPUs  on    Used  for  Photovoltaic  cells  &  Semiconductors   Tianhe-­‐1A  GPU  Supercomputer   2
  3. 3. World’s First Whole H1N1 Virus Simulation More accurate & complete modelFurthers understanding of drug interactions Mole-8.5GPU Supercomputer at CAS-IPE 3
  4. 4. ASUCA TeraFlop Scaling (Weather Modeling) 3990 Tesla M2050s 145.0 Tflops SP 76.1 Tflops DP Before GPUs After GPUs Simulation on Tsubame 2.0, TiTech Supercomputer 4
  5. 5. 2011 Gordon Bell Prize Winner Tsubame 2.0 GPU Supercomputer“Peta-scale Phase-Field Simulation for DendriticSolidification on the TSUBAME 2.0 Supercomputer” Science Impact -- Shimokawabe et. al. Developing lightweight material for fuel efficient cars 5
  6. 6. Forecasting Heart Attacks Tsubame 2.0 GPU SupercomputerPlaque rupture leads to heart attackForecast where/when plaques form 6
  7. 7. Metagenomics Tsubame 2.0 GPU Supercomputer GHOSTM: GPU-based SoftwareBLASTX: Standard CPU Software compatible with BLASTX 7
  8. 8. NAMD Scaling on Tsubame 2.0 8
  9. 9. LAMMPS: Billion Atoms Simulation Billion  Atom  Lennard-­‐Jones  Benchmark   103  Seconds   29  Seconds   288  GPUs  +  CPUs   1920  x86  CPUs   Test  Pla)orm:     NCSA  Lincoln  Cluster  with  S1070  1U  GPU  servers  a?ached       CPU-­‐only  Cluster-­‐  Cray  XT5   9
  10. 10. Protein-DNA Docking Dr. Bo Hong, George Tech Dr. Juntao Guo, UNC Charlotte Improving Prediction Accuracy of Protein-DNA Docking with GPU Computing, Best Paper Award, IEEE BIBM 2011 10
  11. 11. Strong Scaling LQCD: Chroma & MILC 256 GPUs outperform 8K CPU coresChroma 3.41.0 using GCR-DD solver MILC 7.6.3 using mixed-precision CG solver Guochun Shi (NCSA), Balint Joo (Jefferson Labs), Ron Babich (BU), Mike Clark (Harvard), Rich Brower (BU), Steve Gottlieb (Indiana), “Scaling Lattice QCD beyond 100 GPUs,” SC11, ACM (Nov 2011) 11
  12. 12. Computational Fluid Dynamics Scaling on GPUs Navier Stokes (Weak Scaling) in GFLOPS 2.4 Tflops 2000 2432 GFLOPS (logarithmic) 1478 854 128 GPUs 428 228 200 10211x Speedup with GPUs 69 33 20 1 2 4 8 16 32 64 128 Number of GPUs Incompressible Flow Computations, Navier-Stokes 64 Compute nodes with 128 M1060 GPUs Boise State Univeristy, Jacobsen, Thibault, Senocak 48th AIAA Aerospace Sciences Meeting, January 4-7, 2010 12
  13. 13. Titan at Oak Ridge World’s Top Open Science Computing Research Facility 18,000 Tesla GPUs 20+ PetaFlops~90% of flops from GPUs 2x Faster, 3x More Energy Efficient than Current #1 (K Computer) 13
  14. 14. NCSA Mixes GPUs into Blue Waters“   NCSA  is  excited  about  the  inclusion  of  NVIDIAs  Tesla  GPUs  in  Blue   Waters.    GPUs  provide  extraordinary  capabiliWes  for  numerically-­‐ intensive  computaWons  and  a  cost-­‐effecWve,  energy-­‐efficient  way   to  build  tomorrows  petascale  supercomputers.  ”   Thom  Dunning   Director,  NCSA   14

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