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Efficient Sparse Matrix Multiplication on GPU for Large Social Network Analysis

Title
Efficient Sparse Matrix Multiplication on GPU for Large Social Network Analysis
Author
김상욱
Keywords
GPU; Social network analysis; Sparse matrix multiplication
Issue Date
2015-10
Publisher
ACM CIKM
Citation
International Conference on Information and Knowledge Management, Proceedings Volume 19-23-Oct-2015, 17 October 2015, Page. 1261-1270
Abstract
As a number of social network services appear online recently, there have been many attempts to analyze social networks for extracting valuable information. Most existing methods first represent a social network as a quite sparse adjacency matrix, and then analyze it through matrix operations such as matrix multiplication. Due to the large scale and high complexity, efficient processing multiplications is an important issue in social network analysis. In this paper, wepropose aGPU-based method for efficient sparse matrix multiplication through the parallel computing paradigm. The proposed method aims at balancing the amount of workload both at fine- and coarse-grained levels for maximizing the degree of parallelism in GPU. Through extensive experiments using synthetic and real-world datasets, we show that the proposed method outperforms previous methods by up to three orders-of-magnitude. © 2015 ACM.
URI
http://dl.acm.org/citation.cfm?doid=2806416.2806445http://hdl.handle.net/20.500.11754/28136
ISBN
978-145033794-6
DOI
10.1145/2806416.2806445
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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