GPU-accelerated single-cell analysis at scale with rapids-singlecell

TL;DR

rapids-singlecell is a GPU-accelerated framework that dramatically speeds up large-scale single-cell data analysis, enabling real-time, interactive exploration and hypothesis testing on datasets with tens of millions of cells.

Contribution

It introduces a GPU-based analysis pipeline integrated with the scverse ecosystem, achieving up to several hundred-fold speedups over CPU methods for standard single-cell workflows.

Findings

Achieves up to several hundred-fold speedup in analysis workflows.

Reduces analysis time from hours to minutes on standard hardware.

Enables real-time, interactive analysis of large-scale single-cell datasets.

Abstract

Single-cell sequencing technologies reveal cellular heterogeneity at high resolution, advancing our understanding of biological complexity. As datasets start to scale to tens of millions of cells, computational workflows face substantial bottlenecks, with CPU-based analytical pipelines requiring hours or days for routine processing steps like filtering, normalization, and clustering. These scalability limitations fundamentally restrict common interactive data exploration and iterative hypothesis testing. Here we introduce rapids-singlecell, a GPU-accelerated framework that integrates natively with the scverse ecosystem and operates directly on the AnnData data structure, which delivers orders-of-magnitude speedups for single-cell workflows. Built on CuPy arrays and the NVIDIA CUDA-X Data Science (RAPIDS) ecosystem, rapids-singlecell provides near drop-in GPU replacements for core scanpy-based analysis steps. Across standard single-cell workflows such as preprocessing, dimensionality reduction, neighborhood graph construction, clustering, and batch correction, rapids-singlecell achieves speedups of up to several hundred-fold compared to optimized CPU baselines. This reduces analysis time from hours to minutes on standard hardware, while maintaining consistent biological interpretations. These performance improvements make it possible to analyze large data sets in close to real time, without the need for data splitting. Together with real-time parameter tuning and iterative workflows, rapids-singlecell makes interactive large-scale single-cell analysis possible.