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DTSTART;TZID=America/Denver:20231112T164700
DTEND;TZID=America/Denver:20231112T165200
UID:submissions.supercomputing.org_SC23_sess435_ws_pdswwip106@linklings.co
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SUMMARY:Advancing Automated I/O Analysis with Multi-Perspective Views
DESCRIPTION:Workshop\n\nIzzet Yildirim (Illinois Institute of Technology),
  Hariharan Devarajan (Lawrence Livermore National Laboratory (LLNL)), Anth
 ony Kougkas and Xian-He Sun (Illinois Institute of Technology), and Kathry
 n Mohror (Lawrence Livermore National Laboratory (LLNL))\n\nReal-world HPC
  workloads, including simulations and machine learning, place significant 
 strain on storage infrastructure due to their data-dependency, exacerbated
  by the diverse storage options in modern HPC environments, leading to I/O
  bottlenecks. To mitigate these bottlenecks, past analysis methods relied 
 on manual evaluations and tools like Darshan for I/O trace collection, oft
 en necessitating expert involvement and substantial time commitments. In r
 esponse to the imperative for automation due to the time-intensive nature 
 of manual analysis and the pressing need to effectively mitigate I/O bottl
 enecks, analysis tools were developed. According to our findings, these to
 ols, while providing automation, can still benefit from transitioning from
  heuristics-based approaches to a more data-driven decision-making. To add
 ress this, we propose a data-driven approach that leverages multi-perspect
 ive views.\n\nTag: Data Analysis, Visualization, and Storage, Data Movemen
 t and Memory\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs
 : Suren Byna (Ohio State University, Lawrence Berkeley National Laboratory
  (LBNL)); Jay Lofstead (Sandia National Laboratories); Bing Xie (Microsoft
  Corporation); and Amelie Chi Zhou (Hong Kong Baptist University)
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