Combining Program Analysis and Empirical Search to Optimize Programs

Abstract

Code optimization improves program performance through program analysis and program transformation, which transforms the program in an equivalent form. The basis of optimization is data flow analysis and control flow analysis. The paper first analyzes the characterization of Mgrid and the kernel Resid routine, including architecture analysis, data flow analysis, and dependence analysis, which is the basis of static optimization. Based on these characterization data, we first apply static optimization to Resid, including DMA access, data reuse, overlapping of computing and DMA, loop unrolling, common sub-expression extraction, Input Value Embedding, etc. and then loop blocking is applied to Resid, and genetic algorithm is utilized to search for the near-optimal blocking size, which further improves the performance of static optimization. The performance of Mgrid is improved by combing static analysis and empirical search, which is verified during the experiments.