GPU Computing with CUDA

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Performance = Parallelism Efficiency = Locality Fortunately, most applications have lots of both. Amdahl’s law doesn’t apply to most future applications.
Exploiting parallelism and locality requires: Many efficient processors (To exploit parallelism) An exposed storage hierarchy (To exploit locality) A programming system that abstracts this
Page 1 and 2: The Future of GPU Computing Bill Da
Page 3 and 4: Outline Single-thread performance i
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: Scientific Applications Large data
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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