Cybertron: Pushing the Limit on I/O Reduction in Data-Parallel Programs

Tian Xiao, Zhenyu Guo, Hucheng Zhou, Jiaxing Zhang, Xu Zhao, Chencheng Ye, Xi Wang, Wei Lin, Wenguang Chen, and Lidong Zhou

Abstract

I/O reduction has been a major focus in optimizing dataparallel 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 staticanalysis 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.

Details

Publication typeInproceedings
PublisherOOPSLA
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