Brief Announcement: Performance Anomalies in Concurrent Data Structure Microbenchmarks

Authors Rosina F. Kharal, Trevor Brown

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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)


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
  • concurrent microbenchmarks
  • concurrent data structures
  • high performance simulations
  • PRNGs


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