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2018 GTC Washington DC
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DC8139 - Attacking the Opioid Epidemic with Exascale Genomics

Session Speakers
Session Description

We will describe the CoMet application for largescale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and the Custom Correlation Coefficient are implemented using adapted xGEMM kernels optimized for GPU architectures, achieving instruction rates similar to the unmodified kernels. By aggressive overlapping of communications, transfers and computations, and accessing the tensor cores on the Volta GPU, the full computation achieves up to 95 TF per GPU (76% of tensor cores theoretical peak 125 TF) on Summit. 234 x 10^15 element comparisons and 1.88 ExaOps have been reached on 4000 nodes of Summit; full system Summit projected values are 270 x 10^15 comparisons and over 2 ExaOps. Current performance is over 10,000X 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.


Additional Information
AI in Healthcare
AI in Healthcare, Accelerated Data Science, HPC & Supercomputing
Government / National Labs, Healthcare & Life Sciences
All technical
Talk
50 minutes
Session Schedule