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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Jiaxing Zhao, Xu Ye, Chencheng Wang, Xi Guo, Zhenyu Zhou, Hucheng Xiao, Tian Zhou, Lidong Lin, Wei Chen, Wenguang |
| Abstract | I/O reduction has been a major focus in optimizing data-parallel programs for big-data processing. While the current state-of-the-art techniques use static program analysis to reduce I/O, Cybertron proposes a new direction that incorporates runtime mechanisms to push the limit further on I/O reduction. In particular, Cybertron tracks how data is used in the computation accurately at runtime to filter unused data at finer granularity dynamically, beyond what current static-analysis based mechanisms are capable of, and to facilitate a new mechanism called constraint based encoding for more efficient encoding. Cybertron has been implemented and applied to production data-parallel programs; our extensive evaluations on real programs and real data have shown its effectiveness on I/O reduction over the existing mechanisms at reasonable CPU cost, and its improvement on end-to-end performance in various network environments. |
| Starting Page | 895 |
| Ending Page | 908 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781450325851 |
| DOI | 10.1145/2660193.2660204 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-10-15 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Data-parallel Execution-equivalent Mapreduce I/o reduction |
| Content Type | Text |
| Resource Type | Article |
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