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DTSTART:19700308T020000
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DTSTART;TZID=America/Chicago:20181111T105500
DTEND;TZID=America/Chicago:20181111T112000
UID:submissions.supercomputing.org_SC18_sess163_ws_works115@linklings.com
SUMMARY:Flux: Overcoming Scheduling Challenges for Exascale Workflows
DESCRIPTION:Dong H. Ahn, Ned Bass, Albert Chu, Jim Garlick, Mark Grondona,
  Stephen Herbein, Joseph Koning, Tapasya Patki, Thomas R. W. Scogland, and
  Becky Springmeyer (Lawrence Livermore National Laboratory) and Michela Ta
 ufer (University of Tennessee)\n\nMany emerging scientific workflows that 
 target high-end HPC systems require complex interplay with the resource an
 d job management software~(RJMS).  However, portable, efficient and easy-t
 o-use scheduling and execution of these workflows is still an unsolved pro
 blem.  We present Flux, a novel, hierarchical RJMS infrastructure that add
 resses the key scheduling challenges of modern workflows in a scalable, ea
 sy-to-use, and portable manner.  At the heart of Flux lies its ability to 
 be nested seamlessly within batch allocations created by other schedulers 
 as well as itself.  Once a hierarchy of Flux instance is created within ea
 ch allocation, its consistent and rich set of well-defined APIs portably a
 nd efficiently support those workflows that can often feature non-traditio
 nal execution patterns such as requirements for complex co-scheduling, mas
 sive ensembles of small jobs and coordination among jobs in an ensemble.\n
 \nTag: Reproducibility, Scientific Computing, Scientific Workflows, Workfl
 ows, HPC, Data Intensive\n\nRegistration Category: Workshop Reg Pass\n\n
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