DSP-CC: I/O Efficient Parallel Computation of Connected Components in Billion-scale Networks
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- Title
- DSP-CC: I/O Efficient Parallel Computation of Connected Components in Billion-scale Networks
- Authors
- Min-Soo Kim; Sangyeon Lee; Han, WS; Himchan Park; Jeong-Hoon Lee
- Date Issued
- 2015-10
- Publisher
- IEEE
- Abstract
- Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are Hash-To-Min and PowerGraph. Hash-To-Min is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. PowerGraph is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, DSP-CC, which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing. Extensive experimental results show that DSP-CC 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/37320
- DOI
- 10.1109/TKDE.2015.2419665
- ISSN
- 1041-4347
- Article Type
- Article
- Citation
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 27, no. 10, page. 2658 - 2671, 2015-10
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