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DTSTART:19700308T020000
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DTSTART:19701101T020000
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DTSTAMP:20260422T000711Z
LOCATION:506
DTSTART;TZID=America/Denver:20231113T103000
DTEND;TZID=America/Denver:20231113T105000
UID:submissions.supercomputing.org_SC23_sess452_ws_isav111@linklings.com
SUMMARY:Extensions to the SENSEI In situ Framework for Heterogeneous Archi
 tectures
DESCRIPTION:Burlen Loring and Gunther H. Webber (Lawrence Berkeley Nationa
 l Laboratory (LBNL)); E. Wes Bethel (San Francisco State University, Lawre
 nce Berkeley National Laboratory (LBNL)); and Michael W. Mahoney (Lawrence
  Berkeley National Laboratory (LBNL); University of California, University
  of California, Berkeley)\n\nThe presence of GPUs and accelerators in rece
 nt super computing\nsystems, so called heterogeneous architectures, has le
 ad to increased\ncomplexity in execution environments and programming mode
 ls\nas well as deeper memory hierarchies on these systems. In this\nwork w
 e discuss challenges that arise in in situ code coupling on\nheterogeneous
  architectures. We present data and execution model\nextensions to the SEN
 SEI in situ framework targeted at effective use\nof systems with heterogen
 eous architectures. We use the new data\nand execution model extensions in
  SENSEI to investigate a number\nof in situ placement and execution config
 urations and analyze the\nimpact these choices have on overall performance
 .\n\nTag: Data Analysis, Visualization, and Storage, Large Scale Systems, 
 Performance Measurement, Modeling, and Tools\n\nRegistration Category: Wor
 kshop Reg Pass\n\nSession Chairs: E. Wes Bethel (San Francisco State Unive
 rsity, Lawrence Berkeley National Laboratory (LBNL)); Nicola Ferrier (Argo
 nne National Laboratory (ANL), University of Chicago); Axel Huebl (Lawrenc
 e Berkeley National Laboratory (LBNL)); Tom Vierjahn (Westphalian Universi
 ty of Applied Sciences); and Sean Ziegeler (US Department of Defense HPC M
 odernization Program, Department of Defense High Performance Computing Mod
 ernization Program (DoD HPCMP))\n\n
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