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DTSTART:19700308T020000
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DTSTAMP:20230124T171524Z
LOCATION:C143-149
DTSTART;TZID=America/Chicago:20221113T093500
DTEND;TZID=America/Chicago:20221113T100000
UID:submissions.supercomputing.org_SC22_sess426_ws_isav106@linklings.com
SUMMARY:Research Perspectives Toward Autonomic Optimization of In Situ Ana
 lysis and Visualization
DESCRIPTION:Workshop\n\nResearch Perspectives Toward Autonomic Optimizatio
 n of In Situ Analysis and Visualization\n\nWang, Dorier, Parashar\n\nIn si
 tu approaches enable performing data analysis/visualization (ana/vis) clos
 e to the data source and running them on the same system. However, variati
 ons in the simulation data and the diversity of underlying HPC environment
 s increase the difficulty of adjusting the in situ processing configuratio
 ns adaptively. Triggers are an emerging strategy that follows the autonomi
 c computing paradigm to optimize when and how to execute in situ ana/vis t
 asks. By inspecting indicators, the trigger can flexibly issue customized 
 control instructions to optimize the execution of in situ ana/vis tasks in
  real-time. This position paper formalizes the elements of the trigger mec
 hanism according to the definition of autonomic computing. It uses the for
 malization as a guideline to summarize the research status of different as
 pects of the trigger mechanism for in situ processing, including (1) where
  to execute ana/vis tasks, (2) resource allocation of ana/vis tasks, and (
 3) when to execute ana/vis tasks.\n\nSession Format: Recorded\n\nTag: Acce
 lerator-based Architectures, Data Analytics, In Situ Processing, Scientifi
 c Computing, Visualization, Workflows\n\nRegistration Category: Workshop R
 eg Pass
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