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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20181221T160728Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181112T120000
DTEND;TZID=America/Chicago:20181112T123000
UID:submissions.supercomputing.org_SC18_sess151_ws_mlhpce116@linklings.com
SUMMARY:Scaling Deep Learning for Cancer with Advanced Workflow Storage In
 tegration
DESCRIPTION:Workshop\nDeep Learning, Machine Learning, Workshop Reg Pass\n
 \nScaling Deep Learning for Cancer with Advanced Workflow Storage Integrat
 ion\n\nWozniak, Davis, Shu, Ozik, Collier...\n\nCancer Deep Learning Envir
 onment (CANDLE) benchmarks and workflows will combine the power of exascal
 e computing with neural network-based machine learning to address a range 
 of loosely connected problems in cancer research.  This application area p
 oses unique challenges to the exascale computing environment.  Here, we id
 entify one challenge in CANDLE workflows, namely, saving neural network mo
 del representations to persistent storage.  In this paper, we provide back
 ground on this problem, describe our solution, the Model Cache, and presen
 t performance results from running the system on a test cluster, ANL/LCRC 
 Blues, and the petascale supercomputer NERSC Cori.  We also sketch next st
 eps for this promising workflow storage solution.
URL:https://sc18.supercomputing.org/presentation/?id=ws_mlhpce116&sess=ses
 s151
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