Evaluating Accuracy and Performance Tradeoffs in GPU Accelerated Single Cell RNA-seq Analysis

Single-cell RNA sequencing (scRNA-seq) has rapidly become a big-data field, with experiments now profiling up to millions of cells in a single study. While GPU-accelerated pipelines (built on frameworks like NVIDIA RAPIDS and CuPy) promise to drastically reduce runtimes, questions remain about the fidelity and reproducibility of their outputs compared to traditional CPU-based workflows. We benchmarked matched CPU and GPU workflows on a public 1.3-million-cell dataset and systematically downsampled subsets. GPUs delivered a dramatic end-to-end speedup of over 10×, but this performance gain was accompanied by a measurable tradeoff in biological output. We found a consistent, moderate level of concordance between CPU and GPU clustering results (Adjusted Rand Index approximately 0.5) across all sample sizes. Furthermore, we demonstrated that clustering fidelity was relative to the platform used to generate the reference “ground truth.” We also found that fidelity did not strongly correlate with sample size, suggesting that platform-specific algorithmic differences and parameter choices, such as a fixed feature space, were more significant drivers of variability than cell number alone. Together, these results indicated that while modern GPUs enabled fast, large-scale scRNA-seq analysis, researchers needed to be aware of the inherent tradeoffs and consider the choice of computational platform as a critical variable impacting the final biological interpretation.