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DTSTAMP:20260522T150119Z
LOCATION:D165
DTSTART;TZID=America/Chicago:20181111T161500
DTEND;TZID=America/Chicago:20181111T164500
UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw106@linklings.com
SUMMARY:Hummingbird: Efficient Performance Prediction for Executing Genomi
 cs Applications in the Cloud
DESCRIPTION:Utsab Ray and Frank Mueller (North Carolina State University) 
 and Amir Bahmani and Vandhana Krishnan (Stanford University)\n\nA major dr
 awback of executing existing genomics pipelines on cloud computing facilit
 ies is that the onus of efficiently executing it on the best configuration
  lies on the user. Lack of knowledge regarding which cloud configuration i
 s best to execute a pipeline often results in an unnecessary increase in c
 ost due to selecting a more expensive cloud tier than needed. Resources in
  the cloud are expensive, so determining the best configuration before act
 ually running the pipeline saves money and time. To this end, we introduce
  Hummingbird, a framework that predicts the best configuration to execute 
 genomics pipelines on Google cloud.\n\nTag: Applications, Deep Learning, E
 xascale\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Tho
 mas J. Barr (Nationwide Children's Hospital); Patricia Kovatch (Icahn Scho
 ol of Medicine at Mount Sinai); and Eric Stahlberg (MD Anderson Cancer Cen
 ter, University of Texas)\n\n
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