abstract.txt

Selecting appropriate workgroup sizes for OpenCL is critical for program performance, and requires knowledge of the underlying hardware, the data being operated on, and the kernel implementation. We propose the use of machine learning-enabled autotuning to automatically predict workgroup sizes for stencil patterns on CPUs and multi-GPUs, using the Algorithmic Skeleton library SkelCL. In an evaluation across 429 combinations of architecture, kernel, and dataset, we find that static tuning of workgroup size achieves only 26% of the optimal performance. Using machine learning and synthetically generated stencil programs, we achieve 92% of this maximum, demonstrating a median 3.79x speedup over the best possible fixed workgroup size.