Applying the Shared-Memory Parallel Programming Model to the Highly Parallel Cluster Computing Environment

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The aim of this study was to make them consistent in highly parallel cluster computing environments, one of which is to reduce parallel programming cost, and the other is to get enough efficiency in parallel execution, by employing shared-memory parallel programming paradigm.At first, we built a cluster system consisting of eight nodes as a platform to carry out various experiments. And we studied OpenMP and MPI, the former is the representative of the shared-memory parallel programming model, while the latter is the most popular message passing library. We then planned to develop a translator which converts an OpneMP program to an MPI distributed-memory parallel program. The difference of OpenMP and MPI models is focused to each data attribute. In other words, in the former, there are shared data to be shared between threads, and that becomes the big factor to reduce the programmer's burden. On the other hand, in MPI, there are only private data which are local to each process, and it was the greatest problem how to manage this difference. We also examined the UPC as another example of the shared-memory parallel programming models, but, including the default data attribute being private, reached the conclusion that the UPC cannot surpass OpenMP from a point of the degree of standardization and the popularity.We used the translator, which we developed in this study, to convert several kinds of OpenMP sample programs into MPI programs and evaluated their parallel execution efficiency. As a result, for the programs which have a high level locality, we could confirm that our translator showed enough performance on the parallel execution efficiency that was even higher than the virtual implementation methods of distributed-shared-memory systems.