BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260522T150115Z
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:Justin Wozniak (Argonne National Laboratory); Philip Davis (Ru
 tgers University); Tong Shu, Jonathan Ozik, and Nicholson Collier (Argonne
  National Laboratory); Manish Parashar (Rutgers University); and Ian Foste
 r, Thomas Brettin, and Rick Stevens (Argonne National Laboratory)\n\nCance
 r Deep Learning Environment (CANDLE) benchmarks and workflows will combine
  the power of exascale computing with neural network-based machine learnin
 g to address a range of loosely connected problems in cancer research.  Th
 is application area poses unique challenges to the exascale computing envi
 ronment.  Here, we identify one challenge in CANDLE workflows, namely, sav
 ing neural network model representations to persistent storage.  In this p
 aper, we provide background on this problem, describe our solution, the Mo
 del Cache, and present performance results from running the system on a te
 st cluster, ANL/LCRC Blues, and the petascale supercomputer NERSC Cori.  W
 e also sketch next steps for this promising workflow storage solution.\n\n
 Tag: Deep Learning, Machine Learning\n\nRegistration Category: Workshop Re
 g Pass\n\n
END:VEVENT
END:VCALENDAR