A communication placement framework with unified dependence and data-flow analysis

Communication placement analysis is an important step in the compilation of data-parallel programs for multiprocessor systems. This paper presents a communication placement framework that minimizes frequency of communication, eliminates redundant communication, and maximizes communication latency hiding. The paper shows how data dependence information can be combined with data-flow analysis to devise simpler and cleaner data-flow problems. It shows how to develop equations for balanced communication placement using a set of uni-directional analyses with an independent equation system for each placement criterion. This structure allows the framework to support vector message pipelining-an important optimization for programs with loop-carried dependences-but, that was not supported by any previous data-flow framework. The paper also describes how other optimizations, such as partially redundant communication elimination and message coalescing, are supported by the framework. Finally, the paper presents experimental results to prove the efficacy of our placement analysis