Exploring the Optimization Space of Dense Linear Algebra Kernels

Abstract

Dense linear algebra kernels such as matrix multiplication have been used as benchmarks to evaluate the effectiveness of many automated compiler optimizations. However, few studies have looked at collectively applying the transformations and parameterizing them for external search. In this paper, we take a detailed look at the optimization space of three dense linear algebra kernels. We use a transformation scripting language (POET) to implement each kernel-level optimization as applied by ATLAS. We then extensively parameterize these optimizations from the perspective of a general-purpose compiler and use a stand-alone empirical search engine to explore the optimization space using several different search strategies. Our exploration of the search space reveals key interaction among several transformations that must be considered by compilers to approach the level of efficiency obtained through manual tuning of kernels.