图500关于OpenSHMEM:使用对过去工作的实际调查来激励新的算法开发

M. Grossman, H. Pritchard, Zoran Budimlic, Vivek Sarkar
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引用次数: 3

摘要

Graph500是用于高性能计算(HPC)的基于图的基准的开放规范。Graph500的核心计算内核是对无向图的广度优先搜索。因此,与许多其他HPC基准测试不同,Graph500的特点是非常不规则和细粒度的计算、内存访问和网络通信。因此,与其他基准测试工作相比,它可以作为现代HPC硬件、软件和算法技术的更现实的压力测试。另一方面,OpenSHMEM是一种开放的、PGAS和SPMD规范的通信模型,用于跨大量处理元素进行通信。OpenSHMEM明确地关注以细粒度通信为特征的应用程序,Graph500就是一个例子。因此,Graph500的通信模式和OpenSHMEM的功能之间存在一种自然的协同作用。在本文中,我们通过在各种OpenSHMEM实现上开发几种Graph500的新实现来探索这种协同作用。我们对分布式Graph500实现中的最新技术进行了回顾,并比较了最新技术与我们自己的基于openshmem的实现之间的性能和可编程性。我们的结果表明,Graph500的BFS内核可以扩展到Edison超级计算机的1,024个节点,相对于该规模下性能最高的参考实现,性能提高了约2.5倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph500 on OpenSHMEM: Using A Practical Survey of Past Work to Motivate Novel Algorithmic Developments
Graph500 is an open specification of a graph-based benchmark for high-performance computing (HPC). The core computational kernel of Graph500 is a breadth-first search of an undirected graph. Unlike many other HPC benchmarks, Graph500 is therefore characterized by heavily irregular and fine-grain computation, memory accesses, and network communication. Therefore, it can serve as a more realistic stress test of modern HPC hardware, software, and algorithmic techniques than other benchmarking efforts. On the other hand, OpenSHMEM is an open, PGAS, and SPMD specification of a communication model for communicating across large numbers of processing elements. OpenSHMEM explicitly focuses on applications characterized by fine-grain communication, of which Graph500 is one example. Therefore, there is a natural synergy between the communication patterns of Graph500 and the capabilities of OpenSHMEM. In this work we explore that synergy by developing several novel implementations of Graph500 on various OpenSHMEM implementations. We contribute a review of the state-of-the-art in distributed Graph500 implementations, as well as a performance and programmability comparison between the state-of-the-art and our own OpenSHMEM-based implementations. Our results demonstrate improved scaling of Graph500's BFS kernel out to 1,024 nodes of the Edison supercomputer, achieving ~2.5x performance improvement relative to the highest performing reference implementation at that scale.
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