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DTSTAMP:20181221T160727Z
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:Workshop\nApplications, Deep Learning, Exascale, Workshop Reg 
 Pass\n\nHummingbird: Efficient Performance Prediction for Executing Genomi
 cs Applications in the Cloud\n\nRay, Mueller, Bahmani, Krishnan\n\nA major
  drawback of executing existing genomics pipelines on cloud computing faci
 lities is that the onus of efficiently executing it on the best configurat
 ion lies on the user. Lack of knowledge regarding which cloud configuratio
 n is best to execute a pipeline often results in an unnecessary increase i
 n cost due to selecting a more expensive cloud tier than needed. Resources
  in the cloud are expensive, so determining the best configuration before 
 actually running the pipeline saves money and time. To this end, we introd
 uce Hummingbird, a framework that predicts the best configuration to execu
 te genomics pipelines on Google cloud.
URL:https://sc18.supercomputing.org/presentation/?id=ws_cafcw106&sess=sess
 147
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