CUDA Accelerated Linpack on Clusters - Nvidia

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T20 DGEMM Efficiency • Why T20 DGEMM 70% PEAK < T10 95% PEAK ? — Faster compute exposes other bottlenecks • Compare with T10 SGEMM — T10 622 GFLOPS peak MAD — 2x Effective resources: bandwidth (102 GB/s *2 = 204 GB/s) — SGEMM performance: 400 GFLOPS (Hand Code) — 400 / 622 = 64%
DGEMM Compute/COPY Analysis
Page 1 and 2: CUDA Accel
Page 3 and 4: LINPACK Benchmark The LINPACK bench
Page 5 and 6: LINPACK Benchmark Solve a dense NxN
Page 7 and 8: NVIDIA Tesla GPU Computing Products
Page 9 and 10: Overlap DGEMM on CPU and GPU // Cop
Page 11 and 12: DGEMM performance on GPU (T10) A DG
Page 13 and 14: Results on workstation SUN Ultra 24
Page 15 and 16: Effect of PCI-e bandwidth Page lock
Page 17 and 18: FERMI T20 GPU
Page 19 and 20: T10 DGEMM 16 • T10 DGEMM algorith
Page 21: T20 DGEMM Performance • Inner Loo
Page 25 and 26: Fermi DGEMM Strategy • Slice Matr
Page 27 and 28: Additional Overlap strategy • Cop
Page 29 and 30: Optimizations - Auto Split • Keep
Page 31 and 32: DTRSM Same basic splitting approach
Page 33 and 34: DTRSM • GPU implimentation based
Page 35 and 36: DTRSM • Repeat: • DTRSM diagona
Page 37 and 38: Results on single node Supermicro 6
Page 39 and 40: Linpack Score (GFL
Page 41 and 42: Nebulae Cluster #2 on Latest Top500
Page 43 and 44: Future Challenges • Compute growi

CUDA Accelerated Linpack on Clusters - Nvidia

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