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DTSTAMP:20260522T150119Z
LOCATION:A2 Ballroom
DTSTART;TZID=America/Chicago:20181115T113000
DTEND;TZID=America/Chicago:20181115T120000
UID:submissions.supercomputing.org_SC18_sess467_gb104@linklings.com
SUMMARY:Attacking the Opioid Epidemic: Determining the Epistatic and Pleio
 tropic Genetic Architectures for Chronic Pain and Opioid Addiction
DESCRIPTION:Wayne Joubert (Oak Ridge National Laboratory); Deborah Weighil
 l (Oak Ridge National Laboratory, University of Tennessee); David Kainer (
 Oak Ridge National Laboratory); Sharlee Climer (University of Missouri, St
  Louis); Amy Justice (Yale University, US Department of Veterans Affairs);
  Kjiersten Fagnan (Lawrence Berkeley National Laboratory, US Department of
  Energy Joint Genome Institute); and Daniel Jacobson (Oak Ridge National L
 aboratory)\n\nWe describe the CoMet application for large-scale epistatic 
 Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High perfo
 rmance is attained by transforming the underlying vector comparison method
 s into highly performant generalized distributed dense linear algebra oper
 ations. The 2-way and 3-way Proportional Similarity metric and Custom Corr
 elation Coefficient are implemented using native or adapted GEMM kernels o
 ptimized for GPU architectures. By aggressive overlapping of communication
 s, transfers and computations, high efficiency with respect to single GPU 
 kernel performance is maintained up to the full Titan and Summit systems. 
 Nearly 300 quadrillion element comparisons per second and over 2.3 mixed p
 recision ExaOps are reached on Summit by use of Tensor Core hardware on th
 e Nvidia Volta GPUs. Performance is four to five orders of magnitude beyon
 d comparable state of the art. CoMet is currently being used in projects r
 anging from bioenergy to clinical genomics, including for the genetics of 
 chronic pain and opioid addiction.\n\n
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