HyDetect: A Hybrid CPU-GPU Algorithm for Community Detection

Bhowmik, A and Vadhiyar, S (2019) HyDetect: A Hybrid CPU-GPU Algorithm for Community Detection. In: 26th Annual IEEE International Conference on High Performance Computing, HiPC 2019, 17-19, December 2019, Hyderabad, pp. 2-11.

PDF int_con_hig_per_com_dat_ana_02-11_2019.pdf - Published Version Restricted to Registered users only Download (386kB) | Request a copy

Abstract

Community detection is an important problem that is widely applied for finding cluster patterns in brain, social, biological and many other kinds of networks. In this work, we propose a divide-and-conquer community detection algorithm for hybrid CPU-GPU systems. The graph representing a network is partitioned among the CPU and GPU devices of a node, and independent community detection using Louvain's algorithm is carried out in both the parts. The communities are iteratively refined by a novel strategy for identifying and moving 'doubtful' vertices between the devices. The resulting accuracy is found comparable with the single device parallel Louvain algorithms. Our hybrid algorithm helped to explore large graphs that cannot be accommodated in a single device. By harnessing the power of GPUs, our hybrid algorithm is able to provide 42-73 smaller execution times over state-of-art CPU-only algorithms. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 26th IEEE International Conference on High Performance Computing, HiPC 2019
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 26th Annual IEEE International Conference on High Performance Computing, HiPC 2019 ; Conference Date: 17 December 2019 Through 20 December 2019; Conference Code:157722
Keywords:	Graphics processing unit; Iterative methods; Population dynamics; Program processors, Cluster patterns; Community detection; Community detection algorithms; Divide and conquer; GPU algorithms; Hybrid algorithms; Hybrid CPU-GPU executions; Novel strategies, Graph algorithms
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	17 Aug 2020 05:37
Last Modified:	17 Aug 2020 05:37
URI:	http://eprints.iisc.ac.in/id/eprint/64833

Actions (login required)

View Item