K计算机和集群上的分布式和并行编程范式

Jérôme Gurhem, Miwako Tsuji, S. Petiton, M. Sato
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引用次数: 5

摘要

在本文中,我们重点研究了大规模多核超级计算机的分布式并行编程范式。我们介绍了YML,一个并行和分布式应用程序的开发和执行环境,它基于在运行时调度的任务组件图,并针对几种中间件进行了优化。然后我们展示了为什么YML可以很好地适应运行在许多核心上的应用程序。这些任务是用PGAS语言XMP基于指令开发的。我们使用YML/XMP实现逐块高斯消去来求解线性系统。我们还使用XMP和MPI实现了它,没有块。ScaLAPACK还通过LU分解创建了密集线性系统解析的非块实现。此外,我们使用不同数量的块和每个任务的进程来运行它。我们发现,在块数量和每个任务的进程数量之间的一个很好的折衷会产生有趣的结果。YML/XMP在K计算机上比XMP获得更快的结果,在cpu集群上接近XMP, MPI和ScaLAPACK。我们的结论是,像YML/XMP这样的并行和分布式多层编程范式可能是极端规模计算的有趣解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed and Parallel Programming Paradigms on the K computer and a Cluster
In this paper, we focus on a distributed and parallel programming paradigm for massively multicore supercomputers. We introduce YML, a development and execution environment for parallel and distributed applications based on a graph of task components scheduled at runtime and optimized for several middlewares. Then we show why YML may be well adapted to applications running on a lot of cores. The tasks are developed with the PGAS language XMP based on directives. We use YML/XMP to implement the block-wise Gaussian elimination to solve linear systems. We also implemented it with XMP and MPI without blocks. ScaLAPACK was also used to created an non-block implementation of the resolution of a dense linear system through LU factorization. Furthermore, we run it with different amount of blocks and number of processes per task. We find out that a good compromise between the number of blocks and the number of processes per task gives interesting results. YML/XMP obtains results faster than XMP on the K computer and close to XMP, MPI and ScaLAPACK on clusters of CPUs. We conclude that parallel and distributed multilevel programming paradigms like YML/XMP may be interesting solutions for extreme scale computing.
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