[inductor][cpp][gemm] cache blocking config for dynamic shapes #133538
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| std::tuple<std::shared_ptr<int64_t[]>, int> get_factors(int64_t number) { | ||
| thread_local std::map<int64_t, std::tuple<std::shared_ptr<int64_t[]>, int>> cache; |
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nit: why do we need to set this cache as thread_local? If multiple threads are running workloads, I think these threads can share these factors since it only relates to the number of threads for division. Is it due to the concern about initializing this cache with multi threads?
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nit: why do we need to set this cache as
thread_local? If multiple threads are running workloads, I think these threads can share these factors since it only relates to the number of threads for division. Is it due to the concern about initializing this cache with multi threads?
Yes, thread_local avoids the lock contention when multiple threads need to access and modify the cache.
jgong5
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
…ch#133538) Pull Request resolved: pytorch#133538 Approved by: https://github.com/leslie-fang-intel ghstack dependencies: pytorch#135277, pytorch#133447 Co-authored-by: Wu, Chunyuan <[email protected]>
ghstack-source-id: 1f927ba Pull Request resolved: pytorch/pytorch#133538
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