StencilMART: Predicting Optimization Selection for Stencil Computations across GPUs

Abstract

Stencil computations are widely used in high performance computing (HPC) applications. Many HPC platforms utilize the high computation capability of GPUs to accelerate stencil computations. In recent years, stencils have become more diverse in terms of stencil order, memory accesses and computation patterns. To adapt diverse stencils to GPUs, a variety of optimization techniques have been proposed such as streaming and retiming. However, due to the diversity of stencil patterns and GPU architectures, no single optimization technique fits all stencils. Besides, it is challenging to choose the most cost-efficient GPU for accelerating target stencils. To address the above problems, we propose StencilMART, an automatic optimization selection framework that predicts the best optimization combination and execution time under a certain parameter setting for stencils on GPUs. Specifically, the StencilMART represents the stencil patterns as binary tensors and neighboring features through tensor assignment and feature extraction. In addition, the StencilMART implements various machine learning methods such as classification and regression that utilize stencil representation and hardware characteristics for execution time prediction. The experiment results show that the StencilMART can achieve accurate optimization selection and performance prediction for various stencils across GPUs.