| Emerging parallel processor designs create a computing paradigm capable of advancing numerous scientific areas, including medicine, data mining, biology, physics, and earth sciences. However, the trends in many-core hardware technology have advanced far ahead of the advances in software technology and programmer productivity. For the most part, scientists and software developers leverage many-core and GPU (Graphical Processing Unit) computing platforms after painstakingly uncovering the inherent task and data-level parallelism in their application. In many cases, the development does not realize the full potential of the parallel hardware. Moreover, often the exploitable resources, such as processor registers and on-chip programmer- controller memories, scale with each new generation of many-core system and software performance drifts over hardware generations.;An opportunity exists to meet the challenges in mapping scientific applications to parallel computer systems through a synthesis of architectural in-sight, profile driven performance analysis, and execution optimization. This thesis explores an analysis and optimization framework that directs code-tuning strategies and applies science to the art of performance optimization for efficient execution on throughput-oriented systems. The framework demonstrates systematic performance gain through profile- driven analysis on three representative scientific kernels on three different throughput-oriented architectures. |
