Polly is the LLVM project's polyhedral loop nest optimizer. Recently, user-directed loop transformation pragmas were proposed based on LLVM/Clang and Polly. The search space exposed by the transformation pragmas is a tree, wherein each node represents a specific combination of loop transformations that can be applied to the code resulting from the parent node's loop transformations. We have developed a search algorithm based on Monte Carlo tree search (MCTS) to find the best combination of loop transformations. Our algorithm consists of two phases: exploring loop transformations at different depths of the tree to identify promising regions in the tree search space and exploiting those regions by performing a local search. Moreover, a restart mechanism is used to avoid the MCTS getting trapped in a local solution. The best and worst solutions are transferred from the previous phases of the restarts to leverage the search history. We compare our approach with random, greedy, and breadth-first search methods on PolyBench kernels and ECP proxy applications. Experimental results show that our MCTS algorithm finds pragma combinations with a speedup of 2.3x over Polly's heuristic optimizations on average.

中文翻译：

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定制的蒙特卡洛树搜索，用于LLVM / Polly的可组合循环优化转换

Polly是LLVM项目的多面循环嵌套优化器。最近，基于LLVM / Clang和Polly提出了用户控制的循环转换实用程序。转换实用程序公开的搜索空间是一棵树，其中每个节点代表循环转换的特定组合，可以将其应用于父节点的循环转换产生的代码。我们已经开发了一种基于蒙特卡罗树搜索（MCTS）的搜索算法，以找到循环变换的最佳组合。我们的算法包括两个阶段：探索树的不同深度处的循环变换以识别树搜索空间中有希望的区域，并通过执行局部搜索来利用这些区域。此外，使用重新启动机制来避免将MCTS困在本地解决方案中。最佳和最差的解决方案是从重新启动的先前阶段转移来的，以利用搜索历史记录。我们将我们的方法与PolyBench内核和ECP代理应用程序上的随机，贪婪和广度优先搜索方法进行了比较。实验结果表明，相对于Polly的启发式优化算法，我们的MCTS算法能够以2.3倍的速度找到实用的组合。