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: Contribution to journalArticlepeer-review


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 g1/42.6× 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 g1/43.9× and g1/44.9×, respectively, on the average.

Original languageEnglish (US)
Pages (from-to)752-768
Number of pages17
JournalACM SIGPLAN Notices
Issue number4
StatePublished - Jun 11 2018


  • Distributed-memory graph analytics
  • GPUs
  • big data
  • communication optimizations
  • heterogeneous architectures

ASJC Scopus subject areas

  • General Computer Science


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