Basic-block Instruction Scheduling Using Reinforcement Learning and Rollouts

Authors

Amy McGovern, University of Massachusetts - Amherst
Eliot Moss, University of Massachusetts - Amherst
Andrew G. Barto, University of Massachusetts - Amherst

Publication Date

2002

Abstract

The execution order of a block of computer instructions on a pipelined machine can make a difference in its running time by a factor of two or more. In order to achieve the best possible speed, compilers use heuristic schedulers appropriate to each specific architecture implementation. However, these heuristic schedulers are time-consuming and expensive to build. We present empirical results using both rollouts and reinforcement learning to construct heuristics for scheduling basic blocks. In simulation, both the rollout scheduler and the reinforcement learning scheduler outperformed a commercial scheduler on several applications.

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Recommended Citation

McGovern, Amy; Moss, Eliot; and Barto, Andrew G., "Basic-block Instruction Scheduling Using Reinforcement Learning and Rollouts" (2002). Computer Science Department Faculty Publication Series. 13.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/13