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Quantifying and reducing execution variance in STM via model driven commit optimization

Published:10 February 2018Publication History
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Abstract

Simplified parallel programming coupled with an ability to express speculative computation is realized with Software Transactional Memory (STM). Although STMs are gaining popularity because of significant improvements in parallel performance, they exhibit enormous variation in transaction execution with non-repeatable performance behavior which is unacceptable in many application domains, especially in which frame rates and responsiveness should be predictable. Thus, reducing execution variance in STM is an important performance goal that has been mostly overlooked. In this work, we minimize the variance in execution time of threads in STM by reducing non-determinism exhibited due to speculation by first quantifying non-determinism and generating an automaton that models the behavior of STM. We used the automaton to guide the STM to a less non-deterministic execution that reduced the variance in frame rate by a maximum of 65% on a version of real-world Quake3 game.

References

  1. Tushar Kumar, Jaswanth Sreeram, Romain Cledat, and Santosh Pande. 2007. A Profile-driven Statistical Analysis Framework for the Design Optimization of Soft Real-time Applications (ESEC-FSE companion '07). ACM, 529--532. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Daniel Lupei, Bogdan Simion, Don Pinto, Matthew Misler, Mihai Burcea, William Krick, and Cristiana Amza. 2010. Towards Scalable and Transparent Parallelization of Multiplayer Games Using Transactional Memory Support (PPoPP '10). ACM, 325--326. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Kaushik Ravichandran, Ada Gavrilovska, and Santosh Pande. 2014. DeSTM: Harnessing Determinism in STMs for Application Development (PACT '14). ACM, 213--224. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Quantifying and reducing execution variance in STM via model driven commit optimization

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    • Published in

      cover image ACM SIGPLAN Notices
      ACM SIGPLAN Notices  Volume 53, Issue 1
      PPoPP '18
      January 2018
      426 pages
      ISSN:0362-1340
      EISSN:1558-1160
      DOI:10.1145/3200691
      Issue’s Table of Contents
      • cover image ACM Conferences
        PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
        February 2018
        442 pages
        ISBN:9781450349826
        DOI:10.1145/3178487

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 10 February 2018

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