GPU Computing with CUDA

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The Future of <strong>Computing</strong> Bill Dally Chief Scientist & Sr. VP of Research, NVIDIA Bell Professor of Engineering, Stanford University November 18, 2009
Outline Single-thread performance is no longer scaling Performance = Parallelism Efficiency = Locality Applications have lots of both Machines need lots of cores (parallelism) and an exposed storage hierarchy (locality) A programming system must abstract this The future is even more parallel
Page 1: The Future of GPU Computing Bill Da
Page 5 and 6: Moore’s Law Moore, Electronics 38
Page 7 and 8: The End of ILP Scaling 1e+7 1e+6 1e
Page 9 and 10: Performance (vs. VAX- Single-Thread
Page 11 and 12: Chips are power limited and most po
Page 13 and 14: 4,000 64b FPUs fit on a chip 64b FP
Page 15 and 16: Chips are power limited Most power
Page 17 and 18: Scientific Applications Large data
Page 19 and 20: Exploiting parallelism and locality
Page 21 and 22: Optimize use of scarce bandwidth Pr
Page 23 and 24: Fermi
Page 25 and 26: CUDA Abstracts the GPU Architecture
Page 27 and 28: 146X Interactive visualization of v
Page 29: Ease of Programming Source: Nicolas
Page 32 and 33: CPU scaling ends, GPU continues Sou
Page 34 and 35: Energy and Power Challenge Heteroge
Page 36 and 37:
Conclusion
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GPU Computing with CUDA

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