Automatic Performance Tuning of Stencil Computations on GPUs
Automatic Performance Tuning of Stencil Computations on GPUs
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GPU 上模板计算的自动性能调整
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
T. Abdelrahman
中科院分区：
文献类型：
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作者：
Joseph Garvey;T. Abdelrahman
我们考虑图形处理单元上模板计算的自动性能调优。我们提出了一种策略，该策略使用机器学习来确定使用内存的最佳方式，然后使用启发式方法将剩余的优化分成组，并一次详尽地探索一个组。我们在Nvidia GTX Titan GPU上使用102个综合生成的OpenCL模板内核来评估我们的策略。我们根据自动调优期间探索的配置数量和获得的最佳配置的质量来评估我们的策略。我们探索了使用不同优化分组的两种可选启发式方法。我们表明，相对于空间的随机抽样和专家搜索，我们的策略分别将探索的配置数量减少了80%和84%，同时还找到了性能更好的配置。
We consider automatic performance tuning of stencil computations on Graphics Processing Units. We present a strategy that uses machine learning to determine the best way to use memory followed by a heuristic that divides the remaining optimizations into groups and exhaustively explores one group at a time. We evaluate our strategy using 102 synthetically generated OpenCL stencil kernels on an Nvidia GTX Titan GPU. We assess our strategy both in terms of the number of configurations explored during auto-tuning and the quality of the best configuration obtained. We explore two alternative heuristics that use different groupings of the optimizations. We show that, relative to a random sampling of the space and an expert search, our strategy achieves a reduction in the number of configurations explored of up to 80% and 84% respectively while also finding better performing configurations.