Optimizing Data Decomposition for Data Parallel Programs

A critical issue in achieving the performance of data parallel programs is how to efficiently decompose data across processors. On distributed-memory machines, a good data decomposition should increase processor workload balance and reduce interprocessor communication. Data decomposition consists of data distribution and data alignment. In this paper, we propose a trapezoid data distribution pattern and new data alignment algorithms using alignment graph (AG). Our AG-based alignment framework is unique from other related work because it takes advantage of the effect of optimal expression evaluation with regard to multiple assignment statements.