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That's true when you're comparing a GPU to a single CPU. But when you're comparing a GPU to an entire supercomputer the requirement that the workload has massi
by Symmetry 7y ago
That's true when you're comparing a GPU to a single CPU. But when you're comparing a GPU to an entire supercomputer the requirement that the workload has massive parallelism to use all available resources is present in both.
- gruez 7y agoIt's a little more complicated for that. The main problem is that the way GPUs are designed, their execution units share the same instruction pointer[1]. That's not an issue if you're multiplying matricides, but it's an issue anytime you have branches. Therefore, even when your workload is massively parallel, it doesn't necessarily mean that a GPU cluster would perform nearly as well as a CPU cluster with the same amount of FLOPs. [1] https://en.wikipedia.org/wiki/Thread_block_(CUDA_programming) https://en.wikipedia.org/wiki/Thread_block_(CUDA_programming...