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DTSTAMP:20260422T000713Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231114T100000
DTEND;TZID=America/Denver:20231114T170000
UID:submissions.supercomputing.org_SC23_sess291_rpost182@linklings.com
SUMMARY:Optimizing Workflow Performance by Elucidating Semantic Data Flow
DESCRIPTION:Meng Tang (Illinois Institute of Technology), Nathan R. Tallen
 t (Pacific Northwest National Laboratory (PNNL)), and Anthony Kougkas and 
 Xian-He Sun (Illinois Institute of Technology)\n\nDistributed scientific w
 orkflows are becoming data-intensive, and the data movement through storag
 e systems often causes bottleneck. Therefore, it is critical to understand
  data flow. Many scientific datasets incorporate domain semantics with for
 mats like HDF and NetCDF, enhancing the interpretability and context of th
 e data for analysis. We shed new insights on workflow bottlenecks by under
 standing how semantic data sets flow through storage. We unveil a fresh pe
 rspective with careful runtime measurement, recovering the mapping of doma
 in semantics to low-level I/O operations, and effective visualization and 
 analysis of semantic flows.\n\nTag: Artificial Intelligence/Machine Learni
 ng, Architecture and Networks, Heterogeneous Computing, I/O and File Syste
 ms, Performance Measurement, Modeling, and Tools, Post-Moore Computing, Pr
 ogramming Frameworks and System Software, Quantum Computing\n\nRegistratio
 n Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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