Data-layout optimization based on memory-access-pattern analysis for source-code performance improvement

With the rising impact of the memory wall, selecting the adequate data-structure implementation for a given kernel has become a performance-critical issue. This paper presents a new methodology to solve the data-layout decision problem by adapting an input implementation to the host hardware-memory hierarchy. The proposed method automatically identifies, for a given input software, the most performing data-layout implementation for each selected variable by analyzing the memory-access pattern. The proposed method is designed to be embedded within a general-purpose compiler. Experiments on PolybenchC benchmark, recursive-bilateral filter and jpeg-compression kernels, show that our method accurately determines the optimized data structure implementation. These optimized implementations allow reaching an execution-time speed-up up to 48.9X and a L3-miss reduction up to 98.1X, on an X86 processor implementing an Intel Xeon with three levels of data-caches using the least recently used cache-replacement policy.