RL-based selection (Q-Learn and SARSA) can match or approach an oracle's scheduling choices in OpenMP, but only after 144 exploration steps, and the reward function and expert chunk parameter largely determine whether it pays off.
Are static schedules so bad? a case study on cholesky factorization
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A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies
RL-based selection (Q-Learn and SARSA) can match or approach an oracle's scheduling choices in OpenMP, but only after 144 exploration steps, and the reward function and expert chunk parameter largely determine whether it pays off.