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DTSTART;TZID=America/Chicago:20251117T105500
DTEND;TZID=America/Chicago:20251117T112000
UID:submissions.supercomputing.org_SC25_sess217_ws_drbsd114@linklings.com
SUMMARY:Evaluating Accuracy and Performance Tradeoffs in GPU Accelerated S
 ingle Cell RNA-seq Analysis
DESCRIPTION:Cory Gardner, Seyun Jeong, and Oam Khatavkar (Saint Louis Univ
 ersity); Aiden Moon (Parkway Central High School); and Qinglei Cao and Tae
 -Hyuk Ahn (Saint Louis University)\n\nSingle-cell RNA sequencing (scRNA-se
 q) now profiles millions of cells in a single study, creating major comput
 ational demands. GPU-accelerated pipelines, built on frameworks like NVIDI
 A RAPIDS and CuPy, promise large runtime reductions, but questions remain 
 about reproducibility compared to CPU workflows. We benchmarked matched CP
 U 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 fide
 lity. Clustering concordance between CPU and GPU was moderate (Adjusted Ra
 nd Index ~0.50) across all sample sizes. Importantly, fidelity depended mo
 re on platform-specific algorithms and parameter choices than on dataset s
 ize. Results also showed that "ground truth" cluster definitions were rela
 tive to the platform used. These findings indicate that while GPUs enable 
 scalable, efficient scRNA-seq analysis, researchers must consider the choi
 ce of computational platform as a key factor influencing biological interp
 retation.\n\nRecording: Livestreamed, Recorded\n\nRegistration Category: T
 echnical Program Reg Pass, Workshop Reg Pass\n\nSession Chairs: Sheng Di (
 Argonne National Laboratory (ANL), University of Chicago); Ana Gainaru (Oa
 k Ridge National Laboratory (ORNL)); Kento Sato (RIKEN Center for Computat
 ional Science (R-CCS)); Xin Liang (University of Kentucky); and Jieyang Ch
 en (University of Oregon)\n\n
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