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

本文的研究目标是在高度并行的集群计算环境下，通过采用共享内存并行编程范式，使它们保持一致，一方面降低并行编程的成本，另一方面使并行执行具有足够的效率。并对共享内存并行编程模型的代表作OpenMP和最流行的消息传递库MPI进行了研究。然后，我们计划开发一个翻译器，将OpneMP程序转换为MPI分布式内存并行程序。OpenMP和MPI模型的区别主要集中在每个数据属性上。换句话说，在前者中，有共享数据在线程之间共享，这成为减轻程序员负担的重要因素。另一方面，在MPI中，只有每个进程本地的私有数据，如何管理这种差异是最大的问题。我们还研究了作为共享内存并行编程模型的另一个例子的UPC，但是，包括默认的数据属性是私有的，得出的结论是，从标准化程度和普及度的角度来看，UPC不能超过OpenMP。我们使用翻译器，我们在本研究中开发的几种类型的OpenMP示例程序转换成MPI程序，并评估其并行执行效率。因此，对于具有高级别局部性的程序，我们可以确认，我们的翻译器表现出足够的并行执行效率，甚至高于分布式共享内存系统的虚拟实现方法的性能。

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.