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

Workshop: The 11th International Workshop on Data Analysis and Reduction for Big Scientific Data

Authors: Cory Gardner, Seyun Jeong, and Oam Khatavkar (Saint Louis University); Aiden Moon (Parkway Central High School); and Qinglei Cao and Tae-Hyuk Ahn (Saint Louis University)

Abstract: Single-cell RNA sequencing (scRNA-seq) now profiles millions of cells in a single study, creating major computational demands. GPU-accelerated pipelines, built on frameworks like NVIDIA RAPIDS and CuPy, promise large runtime reductions, but questions remain about reproducibility compared to CPU workflows. We benchmarked matched CPU and GPU pipelines on a 1.3-million-cell dataset and downsampled subsets. GPUs achieved over 10× faster runtimes but at the cost of biological fidelity. Clustering concordance between CPU and GPU was moderate (Adjusted Rand Index ~0.50) across all sample sizes. Importantly, fidelity depended more on platform-specific algorithms and parameter choices than on dataset size. Results also showed that "ground truth" cluster definitions were relative to the platform used. These findings indicate that while GPUs enable scalable, efficient scRNA-seq analysis, researchers must consider the choice of computational platform as a key factor influencing biological interpretation.

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