A Massively Parallel Distributed N-body Application Implemented with HPX

Zahra Khatami, Hartmut Kaiser, Patricia A. Grubel, Adrian Serio, J. Ramanujam
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引用次数: 11

Abstract

One of the major challenges in parallelization is the difficulty of improving application scalability with conventional techniques. HPX provides efficient scalable parallelism by significantly reducing node starvation and effective latencies while controlling the overheads. In this paper, we present a new highly scalable parallel distributed N-Body application using a future-based algorithm, which is implemented with HPX. The main difference between this algorithm and prior art is that a future-based request buffer is used between different nodes and along each spatial direction to send/receive data to/from the remote nodes, which helps removing synchronization barriers. HPX provides an asynchronous programming model which results in improving the parallel performance. The results of using HPX for parallelizing Octree construction on one node and the force computation on the distributed nodes show the scalability improvement on an average by about 45% compared to an equivalent OpenMP implementation and 28% compared to a hybrid implementation (MPI+OpenMP) [1] respectively for one billion particles running on up to 128 nodes with 20 cores per each.
用HPX实现的大规模并行分布式n体应用
并行化的主要挑战之一是难以用传统技术提高应用程序的可伸缩性。HPX通过在控制开销的同时显著减少节点饥饿和有效延迟,提供了高效的可扩展并行性。在本文中,我们提出了一个新的高度可扩展的并行分布式N-Body应用程序,该应用程序使用基于未来的算法,该算法由HPX实现。该算法与现有技术之间的主要区别在于,在不同节点之间以及沿着每个空间方向使用基于未来的请求缓冲区来向远程节点发送/接收数据,这有助于消除同步障碍。HPX提供了一种异步编程模型,从而提高了并行性能。使用HPX在一个节点上并行化八叉树构建和在分布式节点上进行力计算的结果显示,与同等的OpenMP实现相比,可扩展性平均提高了45%,与混合实现(MPI+OpenMP)相比,可扩展性分别提高了28%[1],在多达128个节点上运行10亿个粒子,每个节点有20个核心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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