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Title: Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction

Abstract

We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, 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 precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [3];  [4]; ORCiD logo [1]
  1. ORNL
  2. University of Missouri, St. Louis
  3. Veterans Administration
  4. JGI
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1506817
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis - Dallas, Texas, United States of America - 11/11/2018 10:00:00 AM-11/16/2018 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Joubert, Wayne, Weighill, Deborah A., Kainer, David, Climer, Sharlee, Justice, Amy, Fagnan, Kjiersten, and Jacobson, Daniel A. Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction. United States: N. p., 2018. Web. doi:10.1109/SC.2018.00060.
Joubert, Wayne, Weighill, Deborah A., Kainer, David, Climer, Sharlee, Justice, Amy, Fagnan, Kjiersten, & Jacobson, Daniel A. Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction. United States. doi:10.1109/SC.2018.00060.
Joubert, Wayne, Weighill, Deborah A., Kainer, David, Climer, Sharlee, Justice, Amy, Fagnan, Kjiersten, and Jacobson, Daniel A. Thu . "Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction". United States. doi:10.1109/SC.2018.00060. https://www.osti.gov/servlets/purl/1506817.
@article{osti_1506817,
title = {Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction},
author = {Joubert, Wayne and Weighill, Deborah A. and Kainer, David and Climer, Sharlee and Justice, Amy and Fagnan, Kjiersten and Jacobson, Daniel A.},
abstractNote = {We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, 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 precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.},
doi = {10.1109/SC.2018.00060},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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