Minimizing data and synchronization costs in one-way communication

Minimizing communication and synchronization costs is crucial to the realization of the performance potential of parallel computers. This paper presents a general technique which uses a global data-flow framework to optimize communication and synchronization in the context of the one-way communication model. In contrast to the conventional send/receive message-passing communication model, one-way communication is a new paradigm that decouples message transmission and synchronization. In parallel machines with appropriate low-level support, this may open up new opportunities not only to further optimize communication, but also to reduce the synchronization overhead. We present optimization techniques using our framework for eliminating redundant data communication and synchronization operations. Our approach works with the most general data alignments and distributions in languages like High Performance Fortran (HPF) and uses a combination of the traditional data-flow analysis and polyhedral algebra. Empirical results for several scientific benchmarks on a Cray T3E multiprocessor machine demonstrate that our approach is successful in reducing the number of data (communication) and synchronization messages, thereby reducing the overall execution times.