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DTSTAMP:20260422T000711Z
LOCATION:704-706
DTSTART;TZID=America/Denver:20231112T155000
DTEND;TZID=America/Denver:20231112T161400
UID:submissions.supercomputing.org_SC23_sess437_misc289@linklings.com
SUMMARY:Invited Talk: I/O Profiling and Benchmarking for AI Applications
DESCRIPTION:Huihuo Zheng (Argonne National Laboratory (ANL))\n\nTraining a
 rtificial intelligence (AI) models involves repeatedly loading large amoun
 ts of datasets. Data loading and transferring can potentially become one o
 f the bottlenecks. AI training has different Input/Output (I/O) patterns c
 ompared with traditional scientific simulations. It is read intensive, inv
 olving heavy metadata operations, complex data formats, random access patt
 erns, multithreading asynchronous I/O, etc. It is crucial to capture the I
 /O behavior and understand the requirement of storage and I/O in these wor
 kloads. In this talk, we will present our efforts in developing profiling 
 tools and benchmark suites to address the need. Our study shines light on 
 how to better design storage hardware and I/O software to support AI workl
 oads.\n\nTag: Accelerators, Codesign, Heterogeneous Computing, Task Parall
 elism\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Tong 
 Shu (University of North Texas), Seung-Hwan Lim (Oak Ridge National Labora
 tory (ORNL)), Pavan Balaji (Meta AI), and Sanjukta Bhowmick (University of
  North Texas)\n\n
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