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DTSTART:19700308T020000
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DTSTAMP:20211207T055412Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211114T153000
DTEND;TZID=America/Chicago:20211114T155500
UID:submissions.supercomputing.org_SC21_sess429_ws_p3hpc111@linklings.com
SUMMARY:Evaluating Performance and Portability of a Core Bioinformatics Ke
 rnel on Multiple Vendor GPUs
DESCRIPTION:Workshop\n\nEvaluating Performance and Portability of a Core B
 ioinformatics Kernel on Multiple Vendor GPUs\n\nHaseeb, Ding, Deslippe, Aw
 an\n\nTraditional scientific simulations have dominated the workloads of h
 igh-performance computing infrastructures across the world. With recent ad
 vancement in data generation capabilities of systems-biology equipment, a 
 rise in bioinformatics workloads has been observed. Bioinformatics applica
 tions deploy algorithmic motifs that are unique to this domain and place u
 nique requirements on modern programming environments as well as GPU accel
 erators. In this paper, we evaluate the performance and portability of a c
 ore bioinformatics kernel that performs DNA and protein sequence alignment
 s in several bioinformatics software pipelines. Our study evaluates the ab
 ility of multiple vendor GPUs and their best suited programming models to 
 support these algorithms. We use a highly optimized adaptation of sequence
  alignment kernel and find the most performant and productive way of porti
 ng it across multiple vendor GPUs. Methods used in this paper and the insi
 ghts drawn from those may prove useful for design and optimization of prog
 ramming models and hardware accelerators.\n\nTag: Online Only, Heterogeneo
 us Systems, Parallel Programming Languages and Models, Performance, Produc
 tivity Tools, Software Engineering\n\nRegistration Category: Workshop Reg 
 Pass
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