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DTSTART;TZID=America/Chicago:20251117T113000
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UID:submissions.supercomputing.org_SC25_sess202_ws_pdsw110@linklings.com
SUMMARY:Parallel Data Object Creation: Scalable Metadata Management in Par
 allel I/O Library
DESCRIPTION:Youjia Li (Northwestern University); Robert Latham and Robert 
 Ross (Argonne National Laboratory (ANL)); and Ankit Agrawal, Alok Choudhar
 y, and Wei-keng Liao (Northwestern University)\n\nHigh-level I/O libraries
 , such as PnetCDF and HDF5, are commonly used by large-scale scientific ap
 plications to perform I/O tasks in parallel. These I/O libraries store the
  metadata of data objects in files along with their raw data. To ensure me
 tadata consistency during parallel data object creation, they require appl
 ications to call the metadata APIs collectively using consistent metadata.
  Such a requirement can result in an expensive consistency check, as its c
 ost increases with the metadata volume and the number of processes. To add
 ress this limitation, we propose a new file header format, which uses part
 itioned metadata blocks to enable independent data object creation and red
 uce the objects required for consistency check. Our performance evaluation
  shows that this new design achieves a scalable performance, cutting data 
 object creation times by up to 196x when running on 4096 MPI processes to 
 create 5,684,800 data objects in parallel.\n\nTag: Data Analytics, High Pe
 rformance I/O, Storage, Archive, & File Systems, Storage\n\nRecording: Liv
 estreamed, Recorded\n\nRegistration Category: Technical Program Reg Pass, 
 Workshop Reg Pass\n\nSession Chairs: Suren Byna (The Ohio State University
 , Lawrence Berkeley National Laboratory (LBNL)); Anthony Kougkas (Illinois
  Institute of Technology, Argonne National Laboratory (ANL)); Sarah Neuwir
 th (Johannes Gutenberg University Mainz); Ricardo Macedo (INESC TEC, Unive
 rsity of Minho); Jay Lofstead (Sandia National Laboratories, University of
  New Mexico); and Dean Hildebrand (Google LLC)\n\n
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