Statistical Modeling of Control Flow for Speculative Helper Thread Optimization

This paper describes a framework for modeling program behavior and applies it to optimizing prescient instruction prefetch–a novel technique that uses helper threads to improve singlethreaded application performance. Spawn-target pair selection is optimized by modeling program behavior with Markov chains and analyzing them with path expression mappings. Mappings for reaching, and posteriori probability; path length mean, and variance; and path footprint are presented. A limit study demonstrates speedups of 4.8% to 17% for benchmarks with high I-cache miss rates. The framework has been applied to data prefetch helper threads and is potentially applicable to other thread speculation techniques.