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DTSTART;TZID=America/Chicago:20251118T080000
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UID:submissions.supercomputing.org_SC25_sess537_drs117@linklings.com
SUMMARY:Sketch-Based Algorithmic Frameworks for Genome-Scale Mapping
DESCRIPTION:Tazin Rahman (Washington State University)\n\nSketching is a w
 idely used class of techniques aimed at generating compact representations
  of longer biological sequences. Instead of comparing sequences, sketches 
 allow us to sample from a subspace of k-mers and use those samples for com
 parison, saving both time and memory in the end application. One of the ke
 y metrics to consider here is density, which refers to the fraction of the
  sampled k-mers retained by the sketch. While a lower density is preferabl
 e for space considerations, it could also impact the sensitivity of the ma
 pping process. \n\nIn this work, we study sketch-based data sparsification
  with high performance computing to improve scalability in mapping. Our co
 ntributions are twofold: 1) we present a scalable parallel algorithmic fra
 mework for alignment-free mapping, called JEM-mapper, and 2) we present a 
 sketch library called MHSketch by extending JEM-mapper to adopt different 
 sequence sketching schemes. Experimental evaluation demonstrates the abili
 ty of our approach to significantly reduce density and reap performance be
 nefits from it. In particular, results show that MHSketch achieves accurat
 e mapping while reducing time-to-solution (speedups between 2.2x to 9.3x),
  and drastically reducing memory usage (>92% savings) compared to other to
 ols.\n\nTag: Research & ACM SRC Posters\n\nRecording: Not Livestreamed, No
 t Recorded\n\nRegistration Category: Technical Program Reg Pass\n\nSession
  Chairs: Kento Sato (RIKEN Center for Computational Science (R-CCS)); Chri
 s Schlipalius (Pawsey Supercomputing Research Centre; Commonwealth Scienti
 fic and Industrial Research Organisation (CSIRO), Australia); and Anja Ger
 bes (Georg-August-Universität Göttingen)\n\n
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