CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

Journal: BMC bioinformatics
Published Date: Dec 21, 2018

Abstract

BACKGROUND: Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines.

Authors

  • Justin M Wozniak
    Argonne National Laboratory, Argonne, IL, USA. woz@anl.gov.
  • Rajeev Jain
    Argonne National Laboratory, Argonne, IL, USA.
  • Prasanna Balaprakash
    Argonne National Laboratory, Argonne, IL, USA.
  • Jonathan Ozik
    Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA.
  • Nicholson T Collier
    Argonne National Laboratory, Argonne, IL, USA.
  • John Bauer
    Argonne National Laboratory, Argonne, IL, USA.
  • Fangfang Xia
    Computing, Environment, and Life Sciences Directorate, Argonne National Laboratory, Argonne, Illinois, USA.
  • Thomas Brettin
    Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA.
  • Rick Stevens
    IBM Watson Health, Cambridge, Massachusetts, USA.
  • Jamaludin Mohd-Yusof
    Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Cristina Garcia Cardona
    Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Brian Van Essen
    Lawrence Livermore National Laboratory, Livermore, CA, USA.
  • Matthew Baughman
    Minerva, San Francisco, CA, USA.

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