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bioRxiv · 10.1101/2022.05.26.493607

Accelerating single-cell genomic analysis with GPUs

Nolet, C.·Lal, A.·Ilango, R.·Dyer, T.·Movva, R.·Zedlewski, J.·Israeli, J.
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Abstract

Single-cell genomic technologies are rapidly improving our understanding of cellular heterogeneity in biological systems. In recent years, technological and computational improvements have continuously increased the scale of single-cell experiments, and now allow for millions of cells to be analyzed in a single experiment. However, existing software tools for single-cell analysis do not scale well to such large datasets. RAPIDS is an open-source suite of Python libraries that use GPU computing to accelerate data science workflows. Here, we report the use of RAPIDS and GPU computing to accelerate single-cell genomic analysis workflows and present open-source examples that can be reused by the community.

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Nolet, C., Lal, A., Ilango, R., Dyer, T., Movva, R., Zedlewski, J., Israeli, J.. 2022-05-28. Accelerating single-cell genomic analysis with GPUs. https://doi.org/10.1101/2022.05.26.493607

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