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// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2025 Weiwei Kong // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #ifndef EIGEN_THREADPOOL_FORKJOIN_H #define EIGEN_THREADPOOL_FORKJOIN_H // IWYU pragma: private #include "./InternalHeaderCheck.h" namespace Eigen { // ForkJoinScheduler provides implementations of various non-blocking ParallelFor algorithms for unary // and binary parallel tasks. More specifically, the implementations follow the binary tree-based // algorithm from the following paper: // // Lea, D. (2000, June). A java fork/join framework. *In Proceedings of the // ACM 2000 conference on Java Grande* (pp. 36-43). // // For a given binary task function `f(i,j)` and integers `num_threads`, `granularity`, `start`, and `end`, // the implemented parallel for algorithm schedules and executes at most `num_threads` of the functions // from the following set in parallel (either synchronously or asynchronously): // // f(start,start+s_1), f(start+s_1,start+s_2), ..., f(start+s_n,end) // // where `s_{j+1} - s_{j}` and `end - s_n` are roughly within a factor of two of `granularity`. For a unary // task function `g(k)`, the same operation is applied with // // f(i,j) = [&](){ for(Index k = i; k < j; ++k) g(k); }; // // Note that the parameter `granularity` should be tuned by the user based on the trade-off of running the // given task function sequentially vs. scheduling individual tasks in parallel. An example of a partially // tuned `granularity` is in `Eigen::CoreThreadPoolDevice::parallelFor(...)` where the template // parameter `PacketSize` and float input `cost` are used to indirectly compute a granularity level for a // given task function. // // Example usage #1 (synchronous): // ``` // ThreadPool thread_pool(num_threads); // ForkJoinScheduler::ParallelFor(0, num_tasks, granularity, std::move(parallel_task), &thread_pool); // ``` // // Example usage #2 (executing multiple tasks asynchronously, each one parallelized with ParallelFor): // ``` // ThreadPool thread_pool(num_threads); // Barrier barrier(num_async_calls); // auto done = [&](){ barrier.Notify(); }; // for (Index k=0; k static void ParallelForAsync(Index start, Index end, Index granularity, DoFnType&& do_func, DoneFnType&& done, ThreadPoolTempl* thread_pool) { if (start >= end) { done(); return; } thread_pool->Schedule([start, end, granularity, thread_pool, do_func = std::forward(do_func), done = std::forward(done)]() { RunParallelFor(start, end, granularity, do_func, thread_pool); done(); }); } // Synchronous variant of ParallelForAsync. // WARNING: Making nested calls to `ParallelFor`, e.g., calling `ParallelFor` inside a task passed into another // `ParallelFor` call, may lead to deadlocks due to how task stealing is implemented. template static void ParallelFor(Index start, Index end, Index granularity, DoFnType&& do_func, ThreadPoolTempl* thread_pool) { if (start >= end) return; Barrier barrier(1); auto done = [&barrier]() { barrier.Notify(); }; ParallelForAsync(start, end, granularity, do_func, done, thread_pool); barrier.Wait(); } private: // Schedules `right_thunk`, runs `left_thunk`, and runs other tasks until `right_thunk` has finished. template static void ForkJoin(LeftType&& left_thunk, RightType&& right_thunk, ThreadPoolTempl* thread_pool) { typedef typename ThreadPoolTempl::Task Task; std::atomic right_done(false); auto execute_right = [&right_thunk, &right_done]() { std::forward(right_thunk)(); right_done.store(true, std::memory_order_release); }; thread_pool->Schedule(execute_right); std::forward(left_thunk)(); Task task; while (!right_done.load(std::memory_order_acquire)) { thread_pool->MaybeGetTask(&task); if (task.f) task.f(); } } static Index ComputeMidpoint(Index start, Index end, Index granularity) { // Typical workloads choose initial values of `{start, end, granularity}` such that `start - end` and // `granularity` are powers of two. Since modern processors usually implement (2^x)-way // set-associative caches, we minimize the number of cache misses by choosing midpoints that are not // powers of two (to avoid having two addresses in the main memory pointing to the same point in the // cache). More specifically, we choose the midpoint at (roughly) the 9/16 mark. const Index size = end - start; const Index offset = numext::round_down(9 * (size + 1) / 16, granularity); return start + offset; } template static void RunParallelFor(Index start, Index end, Index granularity, DoFnType&& do_func, ThreadPoolTempl* thread_pool) { Index mid = ComputeMidpoint(start, end, granularity); if ((end - start) < granularity || mid == start || mid == end) { do_func(start, end); return; } ForkJoin([start, mid, granularity, &do_func, thread_pool]() { RunParallelFor(start, mid, granularity, do_func, thread_pool); }, [mid, end, granularity, &do_func, thread_pool]() { RunParallelFor(mid, end, granularity, do_func, thread_pool); }, thread_pool); } }; } // namespace Eigen #endif // EIGEN_THREADPOOL_FORKJOIN_H