June 2013
Intermediate to advanced
528 pages
13h 11m
English
Content preview from The CUDA Handbook: A Comprehensive Guide to GPU ProgrammingBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 11. Streaming Workloads
Streaming workloads are among the simplest that can be ported to CUDA: computations where each data element can be computed independently of the others, often with such low computational density that the workload is bandwidth-bound. Streaming workloads do not use many of the hardware resources of the GPU, such as caches and shared memory, that are designed to optimize reuse of data.
Since GPUs give the biggest benefits on workloads with high computational density, it might be useful to review some cases when it still makes sense for streaming workloads to port to GPUs.
• If the input and output are in device memory, it doesn’t make sense to transfer the data back to the CPU just to perform one operation.
• If the ...
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