Implementing Breadth-First Search on a Compact Supercomputer Suiren

Mitsuishi, Takuji; Kaneda, Takahiro; Torii, Sunao; Amano, Hideharu

doi:10.1109/candar.2016.0075

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…As shown in Figure 4, the Matrix-2000+ CPUs adopt a regional autonomous parallel architecture composed of several regions. Each region can be viewed as a functionally-independent SN, which has SVE (Scalable Vector Extension) configured in hardware that can be used to accelerate BFS [12][13][14][15][16][17]. Rather than using a fixed vector length, SVE allows Matrix-2000+ to choose the most appropriate vector length for applications, ranging from 128 bits up to 1024 bits per vector register file.…”

Section: Bfs With Svementioning

confidence: 99%

TianheGraph: Customizing Graph Search for Graph500 on Tianhe Supercomputer

Gan

Zhang

Wang

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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As the era of exascale supercomputing is coming, it is vital for next-generation supercomputers to find appropriate applications with high social and economic benefit. In recent years, it has been widely accepted that extremely-large graph computation is a promising killer application for supercomputing. Although Tianhe series supercomputers are leading in the world-wide competition of supercomputing (ranked No. 1 in the Top500 list for six times), previously they had been inefficient in graph computation according to the Graph500 list. This is mainly because the previous graph processing system cannot leverage the advanced hardware features of Tianhe supercomputers. To address the problem, in this paper we present our integrated optimizations for improving the graph computation performance on our next-generation exascale Tianhe supercomputing system, mainly including sorting with buffering for heavy vertices, vectorized searching with SVE (Scalable Vector Extension) on matrix2000+ CPUs, and group-based monitor communication on the proprietary interconnection network. Performance evaluation on a subset of the Tianhe exascale supercomputer (with 512 nodes and 96608 cores) shows that our customized graph processing system achieves 2131.98 GTEPS, which even outperforms the Tianhe-2 supercomputer (ranked No. 7 in Graph500 by running the state-of-the-art graph processing system) that has 16x more computing nodes.

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