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.
On the Scalability of Dynamic Scheduling Scientific Applications with Adaptive Weighted Factoring
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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.