Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-B618BD23-D64F-4B95-85A5-BF3A993D21C7
Visible to Intel only — GUID: GUID-B618BD23-D64F-4B95-85A5-BF3A993D21C7
Using oneDNN with Threadpool-Based Threading
When oneDNN is built with the threadpool CPU runtime (see Build Options), oneDNN requires the user to implement a threadpool interface to enable the library to perform computations using multiple threads.
The threadpool interface is defined in include/oneapi/dnnl/dnnl_threadpool_iface.hpp. Below is a sample implementation based on the Eigen threadpool that is also used for testing (see tests/test_thread.cpp).
#include "dnnl_threadpool_iface.hpp" class threadpool_t : public dnnl::threadpool_interop::threadpool_iface { private: // Change to Eigen::NonBlockingThreadPool if using Eigen <= 3.3.7 std::unique_ptr<Eigen::ThreadPool> tp_; public: explicit threadpool_t(int num_threads = 0) { if (num_threads <= 0) num_threads = (int)std::thread::hardware_concurrency(); tp_.reset(new Eigen::ThreadPool(num_threads)); } int get_num_threads() const override { return tp_->NumThreads(); } bool get_in_parallel() const override { return tp_->CurrentThreadId() != -1; } uint64_t get_flags() override { return ASYNCHRONOUS; } void parallel_for(int n, const std::function<void(int, int)> &fn) override { int nthr = get_num_threads(); int njobs = std::min(n, nthr); for (int i = 0; i < njobs; i++) { tp_->Schedule([i, n, njobs, fn]() { int start, end; impl::balance211(n, njobs, i, start, end); for (int j = start; j < end; j++) fn(j, n); }); } }; };