https://doi.org/10.1140/epjs/s11734-026-02338-3
Regular Article
AccRQA library: accelerating recurrence quantification analysis
1
Institute of Physics in Opava, Silesian University in Opava, Bezručovo nám. 13, 74601, Opava, Czech Republic
2
Postdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473, Postdam, Germany
a
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Received:
26
January
2026
Accepted:
20
April
2026
Published online:
13
May
2026
Abstract
Recurrence quantification analysis is a powerful tool for identifying and quantifying patterns in dynamical systems, widely used across many disciplines. The rapid growth of data in these fields demands more efficient techniques for analysis. We present AccRQA, a high-performance library available in Python, R, and C/C++, which utilizes novel, scalable, and portable parallel algorithms. AccRQA is parallelized using OpenMP and can leverage NVIDIA GPUs when available, providing portability across computational platforms (CPUs, GPUs) and user environments (PC, HPC), thus offering flexibility between exploration and systematic mapping of a vast parameter space. AccRQA supports long time series and efficient computations for different embedding dimensions m and delay 
with minimal memory requirements. We also present performance benchmarks demonstrating an average 10 
speed-up and at least a 6 speed-up compared to state-of-the-art RQA packages on both CPUs and GPUs.
© The Author(s) 2026
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