This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation.

中文翻译：

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使用重构和蒙特卡罗树搜索对异构并行机进行编程

本文介绍了一种新技术，用于引入和调整异构共享内存系统（包括 CPU 和 GPU 的混合）的并行性，使用算法骨架（如农场和管道）的组合，蒙特卡罗树搜索来推导可用硬件资源的任务，以及重构工具支持以简单有效的方式应用模式和映射。使用我们的方法，我们在多个案例研究中展示了易于获得、显着且可扩展的加速，显示在具有一个 GPU 的 24 核机器上的顺序代码加速高达 41。我们还证明了由 MCTS 算法导出的映射获得的加速比最佳手动并行化的 5-15% 以内。