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DTSTAMP:20260522T150123Z
LOCATION:D168
DTSTART;TZID=America/Chicago:20181112T121000
DTEND;TZID=America/Chicago:20181112T121500
UID:submissions.supercomputing.org_SC18_sess140_ws_isav113@linklings.com
SUMMARY:Leveraging Scalable Event Distribution to Enable Data-Driven In Si
 tu Scientific Workflows
DESCRIPTION:Zhe Wang, Anthony Simonet, Pradeep Subedi, Philip E. Davis, an
 d Manish Parashar (Rutgers University)\n\nNovel event-driven workflow syst
 ems have been effectively used to increase the performance of large-scale 
 scientific applications by removing most of the implicit synchronization r
 equired to orchestrate distributed tasks. However, these event-driven work
 flow systems, by focusing only on events related to the completion of task
 s and data transfers, fail to address the dynamic and irregular workflows 
 that require fine adaptation of the execution to the environment, faults, 
 and to partial results from the application itself.\n\nIn this article, we
  explore the idea of a programming model for irregular and dynamic workflo
 ws that is not only based on task-related events, but also on the intermed
 iate data produced the tasks. We contend that compared to traditional work
 flow execution systems this technique will ease development, increase flex
 ibility and performance by removing implicit synchronization and automatin
 g previously tedious tasks related to workflow steering. We identify the c
 lasses of workflows that will benefit the most from this model and discuss
  design considerations for future implementations. In particular, we discu
 ss how novel in-situ analysis techniques can be leveraged to implement a w
 orkflow system based on events of various natures and origins, from the in
 frastructure to the intermediate data while a workflow is running.\n\nTag:
  Data Analytics, Data Management, Visualization\n\nRegistration Category: 
 Workshop Reg Pass\n\nSession Chairs: Earl P.N. Duque (Intelligent Light); 
 Nicola Ferrier (Argonne National Laboratory (ANL), University of Chicago);
  Kenneth Moreland (Oak Ridge National Laboratory (ORNL)); and Matthew Wolf
  (Oak Ridge National Laboratory (ORNL))\n\n
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