Brief Announcement: Performance Anomalies in Concurrent Data Structure Microbenchmarks

Authors Rosina F. Kharal, Trevor Brown



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Author Details

Rosina F. Kharal
  • University of Waterloo, Canada
Trevor Brown
  • University of Waterloo, Canada

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Rosina F. Kharal and Trevor Brown. Brief Announcement: Performance Anomalies in Concurrent Data Structure Microbenchmarks. In 36th International Symposium on Distributed Computing (DISC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 246, pp. 45:1-45:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.DISC.2022.45

Abstract

Recent decades have witnessed a surge in the development of concurrent data structures with an increasing interest in data structures implementing concurrent sets (CSets). Microbenchmarking tools are frequently utilized to evaluate and compare performance differences across concurrent data structures. The underlying structure and design of the microbenchmarks themselves can play a hidden but influential role in performance results. However, the impact of microbenchmark design has not been well investigated. In this work, we illustrate instances where concurrent data structure performance results reported by a microbenchmark can vary 10-100x depending on the microbenchmark implementation details. We investigate factors leading to performance variance across three popular microbenchmarks and outline cases in which flawed microbenchmark design can lead to an inversion of performance results between two concurrent data structure implementations. We further derive a prescriptive approach for best practices in the design and utilization of concurrent data structure microbenchmarks.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Parallel computing methodologies
Keywords
  • concurrent microbenchmarks
  • concurrent data structures
  • high performance simulations
  • PRNGs

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References

  1. Maya Arbel-Raviv, Trevor Brown, and Adam Morrison. Getting to the root of concurrent binary search tree performance. In 2018 USENIX Annual Technical Conference, 2018. Google Scholar
  2. Trevor Brown, Aleksandar Prokopec, and Dan Alistarh. Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2020. Google Scholar
  3. Tudor David, Rachid Guerraoui, and Vasileios Trigonakis. Asynchronized concurrency: The secret to scaling concurrent search data structures. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems, 2015. Google Scholar
  4. Vincent Gramoli. More than you ever wanted to know about synchronization: Synchrobench, measuring the impact of the synchronization on concurrent algorithms. In Proceedings of the Symposium on Principles and Practice of Parallel Programming, pages 1-10, 2015. Google Scholar
  5. Aravind Natarajan and Neeraj Mittal. Fast concurrent lock-free binary search trees. In Proceedings of the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '14, 2014. Google Scholar
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