Optimizing data locality using array tiling

Data transformation is one of the key optimizations in maximizing cache locality. Traditional data transformation strategies employ linear data layouts, e.g., row-major or column-major, for multidimensional arrays. Although a linear layout matches the linear memory space well in most cases, it can only optimize for self-spatial locality for individual references. In this work, we propose a novel data layout transformation framework that is able to determine a tiled layout for each array in an application program. Tiled layout can exploit the group-spatial locality among different references and improve cache line utilization. In our strategy, the data elements accessed by different references in one loop iteration are placed into a tile and fetched into the same cache line at runtime. This helps minimizing conflict misses in caches. We evaluated our data layout transformation framework using 30 benchmarks on a commercial multicore machine. The experimental results show that our approach outperforms state-of-the-art data transformation strategies and works well with large core counts.