We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing. We build our framework on top of Halide and show that MCTS can outperform the state-of-the-art beam-search algorithm. Unlike beam search, which is guided by greedy intermediate performance comparisons between partial and less meaningful schedules, MCTS compares complete schedules and looks ahead before making any intermediate scheduling decision. We further explore modifications to the standard MCTS algorithm as well as combining real execution time measurements with the cost model. Our results show that MCTS can outperform beam search on a suite of 16 real benchmarks.

中文翻译：

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ProTuner：使用蒙特卡罗树搜索调整程序

我们探索将蒙特卡洛树搜索 (MCTS) 算法应用于一项众所周知的困难任务：用于高性能深度学习和图像处理的调优程序。我们在 Halide 之上构建我们的框架，并表明 MCTS 可以胜过最先进的波束搜索算法。与由部分和不太有意义的调度之间的贪婪中间性能比较引导的波束搜索不同，MCTS 比较完整的调度并在做出任何中间调度决策之前向前看。我们进一步探索对标准 MCTS 算法的修改，以及将实时执行时间测量与成本模型相结合。我们的结果表明，MCTS 在一套 16 个真实基准测试中的表现优于波束搜索。