论文标题
通过蒙特卡洛树搜索和神经网络学习，转移和推荐性能知识
Learning, transferring, and recommending performance knowledge with Monte Carlo tree search and neural networks
论文作者
论文摘要
更改程序以优化其性能是一项完全依赖人类直觉和经验的任务。此外，大规模运作的公司正处于没有人理解控制其系统的代码的阶段，因此，进行更改以提高性能会变得非常困难。在本文中，引入了学习系统，该系统为查找计划的建议更改提供了AI帮助。具体而言，可以通过蒙特卡洛树搜索（MCT）框架有效地制定评估反馈，延迟奖励性能编程域的评估反馈，延迟奖励性能编程域。然后表明，可以使用学习来加快树木搜索计算的计算游戏中建立的方法，可以加快计算推荐的程序更改。用于先前问题的MCTS树的预期效用的估计用于学习在新问题中仍然有效的抽样策略，从而证明了优化知识的转移性。该公式应用于Apache Spark分布式计算环境，并观察到初步结果，构建搜索树以寻找建议所需的时间降低了10倍。
Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.
