PPQSort: Pattern‐Defeating Parallel Quicksort

ABSTRACT This paper presents PPQSort (Pattern‐defeating Parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. This paper describes novel quicksort optimizations, including branchless partitioning and their scalable parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on two different CPU architectures (AMD EPYC, ARM A64FX) and with 7 different synthetic input data distributions. An analysis of its cache behavior is also provided. The results of the experimental evaluation demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort‐based implementations for all tested inputs.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2026-03-24
DOI
https://doi.org/10.1002/cpe.70673
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

PPQSort: Pattern‐Defeating Parallel Quicksort

Concurrency and Computation Practice and Experience
Parallel Computing and Optimization Techniques
article

PPQSort: Pattern‐Defeating Parallel Quicksort

article en

Abstract

ABSTRACT This paper presents PPQSort (Pattern‐defeating Parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. This paper describes novel quicksort optimizations, including branchless partitioning and their scalable parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on two different CPU architectures (AMD EPYC, ARM A64FX) and with 7 different synthetic input data distributions. An analysis of its cache behavior is also provided. The results of the experimental evaluation demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort‐based implementations for all tested inputs.

Concurrency and Computation Practice and ExperienceVol. 38(7)
Czech Technical University in Prague (CZ)
Openalex Percentile: Top 7%
Parallel Computing and Optimization Techniques
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