The performance of multi-threaded applications depends on efficient scheduling of parallel tasks. Manually selecting schedulers is difficult because the best scheduler depends on the application, machine and input. We present a frame-work that automatically selects the best scheduler based on empirical tuning results. We applied our framework to tune eleven applications parallelized using OpenMP, TBB or the Galois system. Depending on the application and machine, we observed up to 4X performance improvement over the default scheduler. We were also able to prune the search space by an order of magnitude while still achieving performance within 16% of the best scheduler.
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