Program optimization strategies for data-parallel many-core processors

Abstract

Program optimization for highly parallel systems has historically been considered an art, with experts doing much of the performance tuning by hand. With the introduction of inexpensive, single-chip, massively parallel platforms, more developers will be creating highly data-parallel applications for these platforms while lacking the substantial experience and knowledge needed to maximize application performance. In addition, hand-optimization even by motivated and informed developers takes a significant amount of time and generally still underutilizes the performance of the hardware by double-digit percentages. This creates a need for structured and automatable optimization techniques that are capable of finding a near-optimal program configuration for this new class of architecture. My work discusses various strategies for optimizing programs on a highly dataparallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications. I then introduce program