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DTSTART;TZID=America/Chicago:20211116T103000
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UID:submissions.supercomputing.org_SC21_sess154_pap495@linklings.com
SUMMARY:Accelerating Large Scale de Novo Metagenome Assembly Using GPUs
DESCRIPTION:Paper\n\nAccelerating Large Scale de Novo Metagenome Assembly 
 Using GPUs\n\nAwan, Hofmeyr, Egan, Ding, Buluc...\n\nMetagenomic workflows
  involve studying uncultured microorganisms directly from the environment.
  These environmental samples when processed by modern sequencing machines 
 yield large and complex datasets which exceed the capabilities of metageno
 mic software. The increasing sizes and complexities of datasets make a str
 ong case for exascale-capable metagenome assemblers. The underlying algori
 thmic motifs, however, are not well suited for GPUs. This poses a challeng
 e since the majority of next generation supercomputers will rely primarily
  on GPUs for computation. In this paper we present the first of its kind G
 PU accelerated implementation of local assembly approach that is an integr
 al part of a widely used large scale metagenome assembler, MetHipMer. Loca
 l assembly uses algorithms that induce random memory accesses and non-dete
 rministic workloads, which make GPU offloading a challenging task. Our GPU
  implementation outperforms the CPU version by about 7x and boosts the per
 formance of MetaHipMer by 42% when running on 64 Summit nodes.\n\nTag: Rep
 roducibility Badge, Applications, Computational Science\n\nRegistration Ca
 tegory: Tech Program Reg Pass\n\nAward Finalist: Best Paper Finalist\n\nRe
 producibility Badges: Artifact Available, Artifact Functional, Results Rep
 roduced
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