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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lee, Jeong-Hoon Han, Wook-Shin Park, Kyungyeol Kim, Min-Soo Lee, Sangyeon Kim, Jinha Yu, Hwanjo |
| 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 Graph-Chi, has been recently proposed. Although Graph-Chi significantly outperforms all representative (disk-based) distributed graph engines, we observe that Graph-Chi 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 multi-core 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-and-slide. 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 TurboGraph consistently and significantly outperforms Graph-Chi by up to four orders of magnitude! Our implementation of TurboGraph is available at ``http://wshan.net/turbograph}" as executable files. |
| Starting Page | 77 |
| Ending Page | 85 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450321747 |
| DOI | 10.1145/2487575.2487581 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-08-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Graph processing Parallelism Big data Pin-and-slide |
| Content Type | Text |
| Resource Type | Article |
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