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UID:submissions.supercomputing.org_SC23_sess291_rpost179@linklings.com
SUMMARY:A Methodology for Accelerating Variant Calling on GPU
DESCRIPTION:Beatrice Branchini, Alberto Zeni, and Marco Santambrogio (Poly
 technic University of Milan)\n\nPointing out genetic mutations is pivotal 
 to enable clinicians to prescribe personalized therapies to their patients
 . Genome Analysis ToolKit's HaplotypeCaller, relying on the Pair Hidden Ma
 rkov Model (PairHMM) algorithm, is one of the most used applications to id
 entify such variants. However, the PairHMM represents the bottleneck for t
 his tool. Deploying such an algorithm on hardware accelerators represents 
 a valuable solution. Nevertheless, State-of-the-Art designs do not have th
 e flexibility to support the length variability of the input sequences and
  are not usable in real-life applicative scenarios. For these reasons, thi
 s work presents a GPU accelerator for the PairHMM capable of supporting se
 quences of any length, thanks to a dynamic memory swap methodology, overco
 ming the limitation of literature solutions. Our accelerator achieves an 8
 154× speedup over the software baseline, surpassing the most-performant St
 ate-of-the-Art design up to 1.6×.\n\nTag: Artificial Intelligence/Machine 
 Learning, Architecture and Networks, Heterogeneous Computing, I/O and File
  Systems, Performance Measurement, Modeling, and Tools, Post-Moore Computi
 ng, Programming Frameworks and System Software, Quantum Computing\n\nRegis
 tration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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