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Title: GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

Abstract

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1236326
Report Number(s):
PNNL-SA-111320
KJ0402000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW 2015), May 25-29, 2016, Hyderabad, India, 604-609
Country of Publication:
United States
Language:
English
Subject:
Architecture optimization, reuse, performance, energy, locality, cache

Citation Formats

Sengupta, Dipanjan, Agarwal, Kapil, Song, Shuaiwen, and Schwan, Karsten. GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems. United States: N. p., 2015. Web. doi:10.1109/IPDPSW.2015.16.
Sengupta, Dipanjan, Agarwal, Kapil, Song, Shuaiwen, & Schwan, Karsten. GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems. United States. doi:10.1109/IPDPSW.2015.16.
Sengupta, Dipanjan, Agarwal, Kapil, Song, Shuaiwen, and Schwan, Karsten. Wed . "GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems". United States. doi:10.1109/IPDPSW.2015.16.
@article{osti_1236326,
title = {GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems},
author = {Sengupta, Dipanjan and Agarwal, Kapil and Song, Shuaiwen and Schwan, Karsten},
abstractNote = {Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.},
doi = {10.1109/IPDPSW.2015.16},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {9}
}

Conference:
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