GATUNER:Genetic Algorithm Applied to Floating-Point Precision Tuning

Abstract

Floating-point arithmetic is widely used in high-precision computing fields such as national defense, aerospace, and finance. Different applications have varying requirements for floating-point computation precision, and how to maximize program performance while meeting precision requirements is a crucial challenge in high-performance program design. Mixed-precision technology is a common optimization strategy to address this issue, which involves using multiple precision types within the same program. However, most existing research on mixed precision tends to fall into local optima and fails to directly provide users with usable mixed-precision programs. Since genetic algorithms are optimization methods with global search capabilities, we propose a genetic algorithm-based mixed-precision tuning method and develop a tool called GATUNER. Specifically, GATUNER first analyzes the variables and statements of the program to construct a dependency graph of the program. Then, it uses the Leiden algorithm to detect community structures in the dependency graph, employs an improved genetic algorithm to search for mixed-precision configurations, and finally automatically generates the corresponding mixed-precision program. We evaluated GATUNER on 14 benchmark tests and application programs. The experimental results show that GATUNER can find valid mixed-precision configurations for 76.79% of the test programs, with an average performance improvement of 31.65% for the found mixed-precision configurations. Compared with HIFPTUNER, ADAPT, and CHEF-FP, the average performance improvement of the mixed-precision configurations found by GATUNER increased by 14.1%, 24.09%, and 15.18% respectively. In addition, compared with PESA, the average performance improvement of the mixed-precision configurations obtained by GATUNER increased by 18.67%.