Data layout optimization for portable performance

Abstract

This paper describes a new approach to managing data layouts to optimize performance for array-intensive codes. Prior research has shown that changing data layouts (e.g., interleaving arrays) can improve performance. However, there have been two major reasons why such optimizations are not widely used in practice: (1) the challenge of selecting an optimized layout for a given computing platform, and (2) the cost of re-writing codes to use different layouts for different platforms. We describe a source-to-source code transformation process that enables the generation of different codes with different array interleavings from the same source program, controlled by data layout specifications that are defined separately from the program. Performance results for multicore versions of the benchmarks show significant benefits on four different computing platforms (up to 22.23× for IRSmk, up to 3.68× for SRAD and up to 1.82× for LULESH). We also developed a new optimization algorithm to recommend a good layout for a given source program and specific target machine characteristics. Our results show that the performance obtained using this algorithm achieves 78%–95% performance of the best manual layout on each platform for different benchmarks (IRSmk, SRAD, LULESH).