AITuning：基于机器学习的运行时间通信库调优工具
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries
摘要：
在这项工作中，我们使用深度强化学习方法来解决调整通信库的问题。强化学习是一种机器学习技术，在解决类似游戏的情况下非常有效。事实上，在通信库中调整一组参数以在并行应用程序中获得更好的性能可以表示为一个游戏：找到提供最佳回报的正确组合/路径。尽管AITuning被设计为与不同的运行时间库一起使用，但我们的工作重点是将其应用于构建在MPI-3之上的OpenCoarrays运行时间通信库。这项工作不仅展示了使用强化学习算法来调整通信库的潜力，而且还展示了MPI-3标准引入的MPI工具信息接口如何被运行时间库有效地使用，以在没有人为干预的情况下提高性能。
英文摘要：
In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better performance in a parallel application can be expressed as a game: Find the right combination/path that provides the best reward. Even though AITuning has been designed to be utilized with different run-time libraries, we focused this work on applying it to the OpenCoarrays run-time communication library, built on top of MPI-3. This work not only shows the potential of using a reinforcement learning algorithm for tuning communication libraries, but also demonstrates how the MPI Tool Information Interface, introduced by the MPI-3 standard, can be used effectively by run-time libraries to improve the performance without human intervention.
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