Spotting code optimizations in data-parallel pipelines through PeriSCOPE

  • Authors:

  • Zhenyu Guo;Xuepeng Fan;Rishan Chen;Jiaxing Zhang;Hucheng Zhou;Sean McDirmid;Chang Liu;Wei Lin;Jingren Zhou;Lidong Zhou

  • Affiliations:

  • Microsoft Research Asia;Microsoft Research Asia and Huazhong University of Science and Technology;Microsoft Research Asia and Peking University;Microsoft Research Asia;Microsoft Research Asia;Microsoft Research Asia;Microsoft Research Asia and Shanghai Jiao Tong University;Microsoft Bing;Microsoft Bing;Microsoft Research Asia

  • Venue:
  • OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
  • Year:
  • 2012

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Abstract

To minimize the amount of data-shuffling I/O that occurs between the pipeline stages of a distributed data-parallel program, its procedural code must be optimized with full awareness of the pipeline that it executes in. Unfortunately, neither pipeline optimizers nor traditional compilers examine both the pipeline and procedural code of a data-parallel program so programmers must either hand-optimize their program across pipeline stages or live with poor performance. To resolve this tension between performance and programmability, this paper describes PeriSCOPE, which automatically optimizes a data-parallel program's procedural code in the context of data flow that is reconstructed from the program's pipeline topology. Such optimizations eliminate unnecessary code and data, perform early data filtering, and calculate small derived values (e.g., predicates) earlier in the pipeline, so that less data--sometimes much less data--is transferred between pipeline stages. We describe how PeriSCOPE is implemented and evaluate its effectiveness on real production jobs.