首页> 外文会议>International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology;ECTI-CON 2010 >Multi-level topology-aware and multi-level parallelism on Grid computing environments: A comparison of Global Arrays and Open MP implementations
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Multi-level topology-aware and multi-level parallelism on Grid computing environments: A comparison of Global Arrays and Open MP implementations

机译:网格计算环境上的多层次拓扑感知和多层次并行性:全局数组和Open MP实现的比较

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The Global Arrays toolkit is a library that allows programmers to write parallel programs that use large arrays distributed across processing nodes through the Aggregate Remote Memory Copy Interface (ARMCI). OpenMP is an application programming interface that supports shared memory multiprocessing on many architectures and platforms. In the Symmetric-Multi Processors (SMP), the Global Arrays toolkit will expose the programmers quite similar to that provided by OpenMP. In this study, we will further our investigation on the performance of a parallel application implemented with the Global Arrays toolkit and OpenMP on Grid computing environment. The investigation focuses on the case that an SMP cluster is included in the Grid computing environment. The multi-level parallelism together with multi-level topology-aware techniques have been used in both implementations. We have found that performance of the evaluating application implemented with Global Arrays technique is comparable to that of the application implemented with OpenMP. This implies that programmer can directly port the Global Arrays application directly to the SMP cluster yet its performance is not dropped compared to the native implementation.
机译:Global Arrays工具箱是一个库,允许程序员编写并行程序,这些程序使用通过聚合远程内存复制接口(ARMCI)分布在处理节点上的大型阵列。 OpenMP是一个应用程序编程接口,支持许多体系结构和平台上的共享内存多处理。在“对称多处理器”(SMP)中,“全局阵列”工具包将向程序员公开与OpenMP提供的程序非常相似的程序。在这项研究中,我们将进一步研究在网格计算环境中使用Global Arrays工具箱和OpenMP实现的并行应用程序的性能。调查的重点是在网格计算环境中包含SMP群集的情况。两种实现中都使用了多级并行性以及多级拓扑感知技术。我们发现,使用全局数组技术实现的评估应用程序的性能与使用OpenMP实现的应用程序的性能相当。这意味着程序员可以直接将Global Arrays应用程序直接移植到SMP群集,但是与本机实现相比,它的性能不会降低。

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