ProTuner：用蒙特卡罗树搜索调优程序
ProTuner: Tuning Programs with Monte Carlo Tree Search
摘要：
我们探索将蒙特卡罗树搜索（MCTS）算法应用于一项众所周知的困难任务：为高性能深度学习和图像处理调整程序。我们建立我们的框架顶部的卤化物，并表明MCTS可以优于国家的最先进的波束搜索算法。不像束搜索，这是由贪婪的部分和不太有意义的时间表之间的中间性能比较引导，MCTS比较完整的时间表，并期待在作出任何中间调度决策。我们进一步探讨修改标准MCTS算法，以及结合实际执行时间的测量与成本模型。我们的研究结果表明，MCTS可以优于束搜索的一套16个真正的基准。
英文摘要：
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.
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