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UID:submissions.supercomputing.org_SC24_sess382_pap443@linklings.com
SUMMARY:Rapid GPU-Based Pangenome Graph Layout
DESCRIPTION:Jiajie Li (Cornell University); Jan-Niklas Schmelzle (Technica
 l University of Munich); Yixiao Du (Cornell University); Simon Heumos (Uni
 versity of Tübingen, Germany); Andrea Guarracino (University of Tennessee 
 Health Science Center); Giulia Guidi (Cornell University); Pjotr Prins and
  Erik Garrison (University of Tennessee Health Science Center); and Zhiru 
 Zhang (Cornell University)\n\nComputational Pangenomics is an emerging fie
 ld that studies genetic variation using a graph structure encompassing mul
 tiple genomes. Visualizing pangenome graphs is vital for understanding gen
 ome diversity. Yet, handling large graphs can be challenging due to the hi
 gh computational demands of the graph layout process. \n\nIn this work, we
  conduct a thorough performance characterization of a state-of-the-art pan
 genome graph layout algorithm, revealing significant data-level parallelis
 m, which makes GPUs a promising option for compute acceleration. However, 
 irregular data access and the algorithm's memory-bound nature present sign
 ificant hurdles. To overcome these challenges, we develop a solution imple
 menting three key optimizations: a cache-friendly data layout, coalesced r
 andom states, and warp merging. Additionally, we propose a quantitative me
 tric for scalable evaluation of pangenome layout quality.\n\nEvaluated on 
 24 human whole-chromosome pangenomes, our GPU-based solution achieves a 57
 .3x speedup over the state-of-the-art multithreaded CPU baseline without l
 ayout quality loss, reducing execution time from hours to minutes.\n\nTag:
  Accelerators, Applications and Application Frameworks, Graph Algorithms, 
 Modeling and Simulation, Numerical Methods\n\nRegistration Category: Tech 
 Program Reg Pass\n\nSession Chair: Wenqian Dong (Oregon State University)\
 n\n
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