Data Layout Transformation Exploiting Memory-Level Parallelism in Structured Grid Many-Core Applications

Abstract

We present automatic data layout transformation as an effective compiler performance optimization for memory-bound structured grid applications. Structured grid applications include stencil codes and other code structures using a dense, regular grid as the primary data structure. Fluid dynamics and heat distribution, which both solve partial differential equations on a discretized representation of space, are representative of many important structured grid applications. Using the information available through variable-length array syntax, standardized in C99 and other modern languages, we enable automatic data layout transformations for structured grid codes with dynamically allocated arrays. We also present how a tool can guide these transformations to statically choose a good layout given a model of the memory system, using a modern GPU as an example. A transformed layout that distributes concurrent memory requests among parallel memory system components provides substantial speedup for structured grid applications by improving their achieved memory-level parallelism. Even with the overhead of more complex address calculations, we observe up to 10.94X speedup over the original layout, and a 1.16X performance gain in the worst case.