Journal
ACM SIGPLAN NOTICES
Volume 53, Issue 4, Pages 752-768Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3192366.3192404
Keywords
Distributed-memory graph analytics; communication optimizations; heterogeneous architectures; GPUs; big data
Categories
Funding
- NSF [1337217, 1337281, 1406355, 1618425, ACI-1445606, 1725322]
- DARPA [FA8750-16-2-0004, FA8650-15-C-7563]
- XSEDE grant [ACI-1548562, TG-CIE170005]
- Stampede system at Texas Advanced Computing Center, University of Texas at Austin
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1618425] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1337217] Funding Source: National Science Foundation
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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 similar to 2.6x 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 similar to 3.9x and similar to 4.9x, respectively, on the average.
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