An Edge Attribute-Wise Partitioning and Distributed Processing of R-GCN Using GPUs

Tokio Kibata, Mineto Tsukada, Hiroki Matsutani

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

Abstract

R-GCN (Relational Graph Convolutional Network) is one of GNNs (Graph Neural Networks). The model tries predicting latent information by considering directions and types of edges in graph-structured data, such as knowledge bases. The model builds weight matrices to each edge attribute. Thus, the size of the neural network increases linearly with the number of edge types. Although GPUs can be used for accelerating the R-GCN processing, there is a possibility that the size of weight matrices exceeds GPU device memory. To address this issue, in this paper, an edge attribute-wise partitioning is proposed for R-GCN. The proposed partitioning divides the model and graph data so that R-GCN can be accelerated by using multiple GPUs. Also, the proposed approach can be applied to sequential execution on a single GPU. Both the cases can accelerate the R-GCN processing with large graph data, where the original model cannot be fit into a device memory of a single GPU without partitioning. Experimental results demonstrate that our partitioning method accelerates R-GCN by up to 3.28 times using four GPUs compared to CPU execution for a dataset with more than 1.6 million nodes and 5 million edges. Also, the proposed approach can accelerate the execution even with a single GPU by 1.55 times compared to the CPU execution for a dataset with 0.8 million nodes and 2 million edges.

Original languageEnglish
Title of host publicationEuro-Par 2020
Subtitle of host publicationParallel Processing Workshops - Euro-Par 2020 International Workshops, 2020, Revised Selected Papers
EditorsBartosz Balis, Dora B. Heras, Laura Antonelli, Andrea Bracciali, Thomas Gruber, Jin Hyun-Wook, Michael Kuhn, Stephen L. Scott, Didem Unat, Roman Wyrzykowski
PublisherSpringer Science and Business Media Deutschland GmbH
Pages122-134
Number of pages13
ISBN (Print)9783030715922
DOIs
Publication statusPublished - 2021
EventWorkshops held at the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020 - Virtual, Online
Duration: 2020 Aug 242020 Aug 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12480 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshops held at the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020
CityVirtual, Online
Period20/8/2420/8/25

Keywords

  • GNN
  • GPU
  • Graph data
  • Knowledge base
  • R-GCN

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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