An open64-based cost analytical model in auto-vectorization

Abstract

Discontinuous references to memory and misalignment of memory access mode can have great impact on program performance in auto-vectorization. Different target-specific architecture may have different influence on vectorization performance. As a popular technology in recent years, the multimedia extension technique is important in the vectorization field. Supported by special processing unit in microprocessors, the SIMD automatic vectorization become available. Compiler targeted to SIMD has been widely used in research. This article has proposed a cost analytical model in automatic vectorization compiler. Based on the analysis of several important factors which impact the performance, this model combining with the SLP technique, evaluates both benefit and cost during vectorization and exists as a guidance to vectorization. Experimental results indicate that to some extent this model can accurately predict benefits for vectorization and guide compiler optimization.