Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Wook-Shin Han | - |
dc.contributor.author | Sangyeon Lee | - |
dc.contributor.author | Kyungyeol Park | - |
dc.contributor.author | Jeong-Hoon Lee | - |
dc.contributor.author | Kim, Min-Soo | - |
dc.contributor.author | Jinha Kim | - |
dc.contributor.author | Hwanjo Yu | - |
dc.date.accessioned | 2018-03-19T09:36:28Z | - |
dc.date.available | 2018-03-19T09:36:28Z | - |
dc.date.created | 2014-12-22 | - |
dc.date.issued | 2013-08-12 | - |
dc.identifier.isbn | 9781450321747 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/6104 | - |
dc.description.abstract | Graphs are used to model many real objects such as social networks and web graphs. Many real applications in various fields require efficient and effective management of large-scale graph structured data. Although distributed graph engines such as GBase and Pregel handle billion-scale graphs, the user needs to be skilled at managing and tuning a distributed system in a cluster, which is a nontrivial job for the ordinary user. Furthermore, these distributed systems need many machines in a cluster in order to provide reasonable performance. In order to address this problem, a disk-based parallel graph engine called GraphChi, has been recently proposed. Although GraphChi significantly outperforms all representative (disk-based) distributed graph engines, we observe that GraphChi still has serious performance problems for many important types of graph queries due to 1) limited parallelism and 2) separate steps for I/O processing and CPU processing. In this paper, we propose a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC. TurboGraph is the first truly parallel graph engine that exploits 1) full parallelism including multicore parallelism and FlashSSD IO parallelism and 2) full overlap of CPU processing and I/O processing as much as possible. Specifically, we propose a novel parallel execution model, called pin-Andslide. TurboGraph also provides engine-level operators such as BFS which are implemented under the pin-And-slide model. Extensive experimental results with large real datasets show that Turbo- Graph consistently and significantly outperforms GraphChi by up to four orders of magnitude Our implementation of TurboGraph is available at http://wshan.net/turbograph as executable files. Copyright © 2013 ACM. | - |
dc.language | English | - |
dc.publisher | ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) | - |
dc.title | TurboGraph: A Fast Parallel Graph Engine Handling Billion-scale Graphs in a Single PC | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1145/2487575.2487581 | - |
dc.identifier.scopusid | 2-s2.0-84990030426 | - |
dc.identifier.bibliographicCitation | ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.77 - 85 | - |
dc.identifier.url | https://www.kdd.org/kdd2013/research-program | - |
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Chicago | - |
dc.citation.endPage | 85 | - |
dc.citation.startPage | 77 | - |
dc.citation.title | ACM SIGKDD Conference on Knowledge Discovery and Data Mining | - |
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