Faster Positional‐Population Counts for AVX2, AVX‐512, and ASIMD

ABSTRACT The positional population count operation pospopcnt counts for an array of ‐bit words how often each of the bits was set. Various applications in bioinformatics, database engineering, and digital processing exist. Building on earlier work by Klarqvist et al., we show how positional population counts can be rapidly computed using SIMD techniques with good performance from the first byte, approaching memory‐bound speeds for input arrays of as little as 4 KiB. Improvements include an improved algorithm structure, better handling of unaligned and very short arrays, as well as faster bit‐parallel accumulation of intermediate results. We provide a generic algorithm description as well as implementations for various SIMD instruction set extensions, including Intel AVX2, AVX‐512, and ARM ASIMD, and discuss the adaptation of our algorithm to other platforms.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2025-11-15
DOI
https://doi.org/10.1002/cpe.70435
Citations
1
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.81
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article

Faster Positional‐Population Counts for AVX2, AVX‐512, and ASIMD

Robert Clausecker, Florian Schintke, Daniel Lemire A
1 citations
Concurrency and Computation Practice and Experience
Genomics and Phylogenetic Studies
0.81
article

Faster Positional‐Population Counts for AVX2, AVX‐512, and ASIMD

Robert Clausecker, Florian Schintke, Daniel Lemire A
article en
1 citations

Abstract

ABSTRACT The positional population count operation pospopcnt counts for an array of ‐bit words how often each of the bits was set. Various applications in bioinformatics, database engineering, and digital processing exist. Building on earlier work by Klarqvist et al., we show how positional population counts can be rapidly computed using SIMD techniques with good performance from the first byte, approaching memory‐bound speeds for input arrays of as little as 4 KiB. Improvements include an improved algorithm structure, better handling of unaligned and very short arrays, as well as faster bit‐parallel accumulation of intermediate results. We provide a generic algorithm description as well as implementations for various SIMD instruction set extensions, including Intel AVX2, AVX‐512, and ARM ASIMD, and discuss the adaptation of our algorithm to other platforms.

Concurrency and Computation Practice and ExperienceVol. 37(27-28)
Université du Québec à Montréal (CA), Zuse Institute Berlin (DE), Université TÉLUQ (CA)
Openalex Percentile: Top 21%
Genomics and Phylogenetic Studies
0.81
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