DMM-GAPBS: Adapting the GAP Benchmark Suite to a Distributed Memory Model

Zach Hansen, Brody Williams, John D. Leidel, Xi Wang, Yong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to the ability of graphs to model diverse real-world scenarios such as social networks, roads, or biological networks, effective graph processing techniques are of critical importance to a wide array of fields. As a consequence of the growth of data volumes, some graphs have already outgrown the memory capacities of single servers. In such cases, it is desirable to partition and keep the entire graph in a distributed memory space into order to bring the resources of a computing cluster to bear on the problem. This approach introduces a number of challenges, such as communication bottlenecks and low hardware utilization. However, it is difficult to effectively measure the impact of innovations addressing these challenges due to a lack of standardization in the domain of distributed graph processing. This research study was inspired by, and builds off of, the widely-used GAP Benchmark Suite (GAPBS), which was developed to provide an effective baseline and consistent set of evaluation methodologies for shared memory multiprocessor graph processing systems. We design and develop a new benchmark suite called DMM-GAPBS, a distributed-memory-model GAPBS. We adapt the GAPBS graph building infrastructure and algorithms, but utilize OpenSHMEM to enable a distributed memory environment, in the hope of providing a modular, extensible baseline for the distributed graph processing community. In order to showcase our design and implementation for processing graphs that cannot fit within a single server, we present the results of executing the DMM-GAPBS benchmark kernels on two large synthetic graphs distributed across sixteen nodes of an enterprise class system.

Original languageEnglish
Title of host publication2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665423694
DOIs
StatePublished - 2021
Event2021 IEEE High Performance Extreme Computing Conference, HPEC 2021 - Virtual, Online, United States
Duration: Sep 20 2021Sep 24 2021

Publication series

Name2021 IEEE High Performance Extreme Computing Conference, HPEC 2021

Conference

Conference2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period09/20/2109/24/21

Keywords

  • Benchmark
  • Distributed Memory
  • GAPBS
  • Graph
  • Performance

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