Pipa: pipelined profiling and analysis on multi-core systems

  • Authors:

  • Qin Zhao;Ioana Cutcutache;Weng-Fai Wong

  • Affiliations:

  • Singapore-MIT Alliance, S hool of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore

  • Venue:
  • Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
  • Year:
  • 2008

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Abstract

Dynamic instrumentation systems are gaining popularity as means of constructing customized program profiling and analysis tools. However, dynamic instrumentation based analysis tools still suffer from performance problems. The overhead of such systems can be broken down into two components - the overhead of dynamic instrumentation and the time consumed in the user-defined analysis tools. While important progress has been made in reducing the performance penalty of the dynamic instrumentation itself, less attention has been paid to the user-defined component. In this paper, we present PiPA - Pipelined Profiling and Analysis, which is a novel technique for parallelizing dynamic program profiling and analysis by taking advantage of multi-core systems. We implemented a prototype of PiPA using the dynamic instrumentation system DynamoRIO. Our experiments show that PiPA is able to speed up the overall profiling and analysis tasks significantly. Compared to the more than 100x slowdown of Cachegrind and the 32x slowdown of Pin dcache, we achieved a mere 10.5x slowdown on an 8-core system.