GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

D. Sengupta, K. Agarwal, S. Song, K. Schwan
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引用次数: 5

Abstract

Recent work on graph analytics has sought to leverage the high performance offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithm and limitations in GPU-resident memory for storing large graphs. The Graph Reduce methods presented in this paper permit a GPU-based accelerator to operate on graphs that exceed its internal memory capacity. Graph Reduce operates with a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model, to achieve high degrees of parallelism supported by methods that partition graphs across GPU and host memories and efficiently move graph data between both. Graph Reduce-based programming is performed via device functions that include gather map, gather reduce, apply, and scatter, implemented by programmers for the graph algorithms they wish to realize. Experimental evaluations for a wide variety of graph inputs, algorithms, and system configuration demonstrate that Graph Reduce outperforms other competing approaches.
GraphReduce:基于加速器的高性能计算系统上的大规模图形分析
最近关于图形分析的工作试图利用GPU设备提供的高性能,但由于图形算法固有的不规则性和GPU驻留内存存储大型图形的限制,挑战仍然存在。本文提出的Graph Reduce方法允许基于gpu的加速器对超出其内部内存容量的图形进行操作。Graph Reduce结合了以边缘为中心和以顶点为中心的collect - apply - scatter编程模型实现,通过在GPU和主机内存之间划分图形并有效地在两者之间移动图形数据的方法来实现高度并行性。基于Graph reduce的编程是通过设备函数执行的,这些设备函数包括gather map、gather reduce、apply和scatter,由程序员为他们希望实现的图算法实现。对各种各样的图输入、算法和系统配置的实验评估表明,graph Reduce优于其他竞争方法。
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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