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by _bxg1
2309 days ago
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None of this is surprising, right? Unless your system has fewer threads than cores (which it probably doesn't even without your program) there will always be some context-switching overhead. It's worth keeping in mind I guess - especially the fact that numpy parallelizes transparently - but generally these results are to be expected. The title is also misleading; it suggests that the wall clock time might be longer for parallel code in certain cases. While not impossible, that isn't what the article covers. |
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That is exactly the case, if CPU is the bottleneck in your already-parallel application. It's a case where we really shouldn't be layering different parallel bits together in one codebase, but might be doing it naively.