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UID:submissions.supercomputing.org_SC23_sess304_rpost163@linklings.com
SUMMARY:Exploring the Impacts of Multiple I/O Metrics in Identifying I/O B
 ottlenecks
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\nHPC systems, driven by the
  rise of workloads with significant data requirements, face challenges in 
 I/O performance. To address this, a thorough I/O analysis is crucial to id
 entify potential bottlenecks. However, the multitude of metrics makes it d
 ifficult to pinpoint the causes of low I/O performance. In this work, we a
 nalyze three scientific workloads using three widely accepted I/O metrics.
  We demonstrate that different metrics uncover different I/O bottlenecks, 
 highlighting the importance of considering multiple metrics for comprehens
 ive I/O analysis.\n\nTag: Artificial Intelligence/Machine Learning, Archit
 ecture and Networks, Heterogeneous Computing, I/O and File Systems, Perfor
 mance Measurement, Modeling, and Tools, Post-Moore Computing, Programming 
 Frameworks and System Software, Quantum Computing\n\nRegistration Category
 : Tech Program Reg Pass, Exhibits Reg Pass\n\n
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