November 2018
Intermediate to advanced
310 pages
7h 54m
English
Content preview from Hands-On GPU Programming with Python and CUDA
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Questions
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In simple_element_kernel_example0.py, we don't consider the memory transfers to and from the GPU in measuring the time for the GPU computation. Try measuring the time that the gpuarray functions, to_gpu and get, take with the Python time command. Would you say it's worth offloading this particular function onto the GPU, with the memory transfer times in consideration?
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In Chapter 1, Why GPU Programming?, we had a discussion of Amdahl's Law, which gives us some idea of the gains we can potentially get by offloading portions of a program onto a GPU. Name two issues that we have seen in this chapter that Amdahl's law does not take into consideration.
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Modify gpu_mandel0.py to use smaller and smaller lattices of complex numbers, ...
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