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There are many architectural differences between CPUs and GPUs that make it difficult to draw the kinds of conclusions suggested by these graphs. Data parallel
by cyrus_ 16y ago
There are many architectural differences between CPUs and GPUs that make it difficult to draw the kinds of conclusions suggested by these graphs.
Data parallel algorithms with high computation-to-communication ratios, good data locality and low local branch divergence will see these kinds of speedups when rewritten for GPUs. This is an important subset of algorithms, but don't expect GPUs to replace CPUs for more general-purpose computation any time soon.
- manvsmachine 16y agoVery true. It is for the same reason that the article shows GPU progress outpacing CPU's in regards to FLOPS. The limited scope of a GPU's functionality allows most of the processor's silicon to be arithmetic units. CPU's, on the other hand, have huge sections of their real estate dedicated to handling conditional logic (something most GPU's fail miserably at), legacy instruction handling, virtualization, etc.