Gluon: A communication-optimizing substrate for distributed heterogeneous graph analytics

Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, Keshav Pingali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces a new approach to building distributed-memory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for graph analytics, to produce D-IrGL, the first multi-GPU distributed-memory graph analytics system. Our experiments were done on CPU clusters with up to 256 hosts and roughly 70,000 threads and on multi-GPU clusters with up to 64 GPUs. The communication optimizations in Gluon improve end-to-end application execution time by ∼2.6× on the average. D-Galois and D-IrGL scale well and are faster than Gemini, the state-of-the-art distributed CPU graph analytics system, by factors of ∼3.9× and ∼4.9×, respectively, on the average.

Original languageEnglish (US)
Title of host publicationPLDI 2018 - Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation
EditorsJeffrey S. Foster, Dan Grossman, Jeffrey S. Foster
PublisherAssociation for Computing Machinery
Pages752-768
Number of pages17
ISBN (Electronic)9781450356985
DOIs
StatePublished - Jun 11 2018
Externally publishedYes
Event39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018 - Philadelphia, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameProceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

Other

Other39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018
CountryUnited States
CityPhiladelphia
Period6/18/186/22/18

Keywords

  • Big data
  • Communication optimizations
  • Distributed-memory graph analytics
  • GPUs
  • Heterogeneous architectures

ASJC Scopus subject areas

  • Software

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  • Cite this

    Dathathri, R., Gill, G., Hoang, L., Dang, H. V., Brooks, A., Dryden, N., Snir, M., & Pingali, K. (2018). Gluon: A communication-optimizing substrate for distributed heterogeneous graph analytics. In J. S. Foster, D. Grossman, & J. S. Foster (Eds.), PLDI 2018 - Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (pp. 752-768). (Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)). Association for Computing Machinery. https://doi.org/10.1145/3192366.3192404