Modeling and Performance Evaluation of TSO-Preserving Binary Optimization

Abstract

Program optimization on multi-core systems must preserve the program memory consistency. This paper studies TSO-preserving binary optimization. We introduce a novel approach to formally model TSO-preserving binary optimization based on the formal TSO memory model. The major contribution of the modeling is a sound and complete algorithm to verify TSO-preserving binary optimization with O(N2) complexity. We also developed a dynamic binary optimization system to evaluate the performance impact of TSO-preserving optimization. We show in our experiments that, dynamic binary optimization without memory optimizations can improve performance by 8.1%. TSO-preserving optimizations can further improve the performance by 4.8% to a total 12.9%. Without considering the restriction for TSO-preserving optimizations, the dynamic binary optimization can improve the overall performance to 20.4%.