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gLeiden: accelerated community detection algorithms using directed and undirected graphs on GPUs.

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|March 16, 2026
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Summary

We developed gLeiden, a GPU-accelerated Leiden algorithm implementation that supports directed graphs and significantly speeds up community detection in large single-cell datasets. This tool offers a high-performance alternative for analyzing scRNA-seq and mass cytometry data.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Data science

Background:

  • Community detection is crucial for analyzing single-cell RNA sequencing (scRNA-seq) and mass cytometry data.
  • Existing methods like the Leiden algorithm face computational challenges with large datasets.
  • Current GPU implementations have limitations, such as supporting only undirected graphs.

Purpose of the Study:

  • To develop a high-performance GPU implementation of the Leiden algorithm.
  • To enable efficient community detection for both directed and undirected graphs.
  • To accelerate the analysis of large-scale biological datasets.

Main Methods:

  • Developed gLeiden, a lightweight CUDA C++ based GPU implementation of the Leiden algorithm.
  • Implemented support for directed graphs, a novel feature for GPU-accelerated Leiden.
  • Optimized for performance on modern Graphics Processing Units (GPUs).

Main Results:

  • gLeiden demonstrates significant speedups, with up to 11-12x faster performance for directed graphs compared to existing implementations.
  • Undirected versions (ucLeiden, ugLeiden) show up to 42x speedup over the Java version.
  • Performance is comparable or superior to cuGraph, especially on larger datasets.

Conclusions:

  • gLeiden provides a powerful and efficient solution for community detection in large biological datasets.
  • The implementation's support for directed graphs adds significant value for data analysis.
  • gLeiden represents a state-of-the-art alternative for accelerating scRNA-seq and mass cytometry data analysis.