- Authors:
- A. McGovern,
- E. Moss,
- A. Barto
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, the rollout scheduler outperformed a commercial scheduler, and the reinforcement learning scheduler performed almost as well as the commercial scheduler.
Cited By
Ashouri A, Killian W, Cavazos J, Palermo G and Silvano C (2018). A Survey on Compiler Autotuning using Machine Learning, ACM Computing Surveys, 51:5, (1-42), Online publication date: 30-Sep-2019.- Yuki T, Renganarayanan L, Rajopadhye S, Anderson C, Eichenberger A and O'Brien K Automatic creation of tile size selection models Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization, (190-199)
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