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DTSTART;TZID=America/Denver:20231112T164700
DTEND;TZID=America/Denver:20231112T165200
UID:submissions.supercomputing.org_SC23_sess435_ws_pdswwip106@linklings.co
 m
SUMMARY:Advancing Automated I/O Analysis with Multi-Perspective Views
DESCRIPTION:Izzet Yildirim (Illinois Institute of Technology), Hariharan D
 evarajan (Lawrence Livermore National Laboratory (LLNL)), Anthony Kougkas 
 and Xian-He Sun (Illinois Institute of Technology), and Kathryn Mohror (La
 wrence Livermore National Laboratory (LLNL))\n\nReal-world HPC workloads, 
 including simulations and machine learning, place significant strain on st
 orage infrastructure due to their data-dependency, exacerbated by the dive
 rse storage options in modern HPC environments, leading to I/O bottlenecks
 . To mitigate these bottlenecks, past analysis methods relied on manual ev
 aluations and tools like Darshan for I/O trace collection, often necessita
 ting expert involvement and substantial time commitments. In response to t
 he imperative for automation due to the time-intensive nature of manual an
 alysis and the pressing need to effectively mitigate I/O bottlenecks, anal
 ysis tools were developed. According to our findings, these tools, while p
 roviding automation, can still benefit from transitioning from heuristics-
 based approaches to a more data-driven decision-making. To address this, w
 e propose a data-driven approach that leverages multi-perspective views.\n
 \nTag: Data Analysis, Visualization, and Storage, Data Movement and Memory
 \n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Suren Byna
  (The Ohio State University, Lawrence Berkeley National Laboratory (LBNL))
 ; Jay Lofstead (Sandia National Laboratories, University of New Mexico); B
 ing Xie (Microsoft Corporation); and Amelie Chi Zhou (Hong Kong Baptist Un
 iversity)\n\n
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