18184

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
Scientific Programming, 2018

@article{garvey2018strategy,

   title={A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs},

   author={Garvey, Joseph D and Abdelrahman, Tarek S},

   year={2018}

}

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We propose and evaluate a novel strategy for tuning the performance of a class of stencil computations on Graphics Processing Units. The strategy uses a machine learning model to predict the optimal way to load data from memory followed by a heuristic that divides other optimizations into groups and exhaustively explores one group at a time. We use a set of 104 synthetic OpenCL stencil benchmarks that are representative of many real stencil computations. We first demonstrate the need for auto-tuning by showing that the optimization space is sufficiently complex that simple approaches to determining a high-performing configuration fail. We then demonstrate the effectiveness of our approach on NVIDIA and AMD GPUs. Relative to a random sampling of the space, we find configurations that are 12%/32% faster on the NVIDIA/AMD platform in 71% and 4% less time respectively. Relative to an expert search, we achieve 5% and 9% better performance on the two platforms in 89% and 76% less time. We also evaluate our strategy for different stencil computational intensities, varying array sizes and shapes, and in combination with expert search.
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