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DTSTART;TZID=America/Denver:20231112T170000
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UID:submissions.supercomputing.org_SC23_sess436_ws_ia101@linklings.com
SUMMARY:TANGO: A GPU-Optimized Traceback Approach for Sequence Alignment A
 lgorithms
DESCRIPTION:LeAnn Lindsey (University of Utah), Muhammad Haseeb (Lawrence 
 Berkeley National Laboratory (LBNL)), Hari Sundar (University of Utah), an
 d Muaaz Awan (Lawrence Berkeley National Laboratory (LBNL))\n\nSequence al
 ignment algorithms play a central role in most bioinformatics software. Ho
 wever, porting these algorithms to GPUs can be challenging due to their re
 liance on irregular memory access patterns and integer-heavy operations. H
 ere we present TANGO, an optimized GPU implementation of the Smith-Waterma
 n (SW) algorithm with a focus on the traceback phase. We leverage stacked 
 diagonal-major indexing and compressed binary representation for efficient
  adaptation of the traceback phase to GPUs. Our proposed implementation ac
 hieves speedups of 12.6x and 9.9x compared to state-of-the-art CPU librari
 es for DNA and protein alignments, respectively. It is the fastest SW libr
 ary for protein alignments on GPU while providing comparable performance t
 o other GPU libraries for DNA. Finally, we integrate TANGO into a large-sc
 ale metagenome assembly software to speed up a production workflow.\n\nTag
 : Algorithms, Applications, Architecture and Networks\n\nRegistration Cate
 gory: Workshop Reg Pass\n\nSession Chairs: Sanjukta Bhowmick (University o
 f North Texas), Vito Giovanni Castellana (Pacific Northwest National Labor
 atory (PNNL)), John Feo (Pacific Northwest National Laboratory (PNNL)), Ma
 rco Minutoli (Advanced Micro Devices), and Antonino Tumeo (Pacific Northwe
 st National Laboratory (PNNL))\n\n
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