Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Min Soo | - |
dc.contributor.author | Lee, Sangyeon | - |
dc.contributor.author | Han, Wook-Shin | - |
dc.contributor.author | Park, Him Chan | - |
dc.contributor.author | Lee, Jeong-Hoon | - |
dc.date.available | 2017-07-11T05:45:21Z | - |
dc.date.created | 2017-04-10 | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/2838 | - |
dc.description.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. © 2015 IEEE. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | DSP-CC-: I/O Efficient Parallel Computation of Connected Components in Billion-Scale Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TKDE.2015.2419665 | - |
dc.identifier.scopusid | 2-s2.0-84941554873 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Knowledge and Data Engineering, v.27, no.10, pp.2658 - 2671 | - |
dc.description.isOpenAccess | FALSE | - |
dc.subject.keywordPlus | Algorithm | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Connected Component | - |
dc.subject.keywordPlus | Connected Components | - |
dc.subject.keywordPlus | Disk-Based | - |
dc.subject.keywordPlus | Disks (Machine Components) | - |
dc.subject.keywordPlus | Graphic Methods | - |
dc.subject.keywordPlus | GRAPHS | - |
dc.subject.keywordPlus | Parallel | - |
dc.subject.keywordPlus | Parallel Algorithms | - |
dc.subject.keywordPlus | SSD | - |
dc.citation.endPage | 2671 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 2658 | - |
dc.citation.title | IEEE Transactions on Knowledge and Data Engineering | - |
dc.citation.volume | 27 | - |
There are no files associated with this item.