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DTSTAMP:20211207T055352Z
LOCATION:223-224
DTSTART;TZID=America/Chicago:20211116T103000
DTEND;TZID=America/Chicago:20211116T120000
UID:submissions.supercomputing.org_SC21_sess239_pan139@linklings.com
SUMMARY:Beyond the Hype: Is There a Typical AI/ML Storage Workload?
DESCRIPTION:Panel\n\nBeyond the Hype: Is There a Typical AI/ML Storage Wor
 kload?\n\nHildebrand, Klimovic, Wang, Kougkas, Newburn...\n\nIs your stora
 ge really optimized for AI/ML? What does that even mean?  There are many c
 laims about AI/ML needs for storage but very few well-defined workload and
  technology requirements.  Traditional HPC parallel file systems are optim
 ized for writing large checkpoints, and cloud object stores are optimized 
 for storing massive datasets, but both are somehow supporting large scale 
 AI/ML workloads.  Are these two very different storage systems really opti
 mized for an I/O workload that didn’t even exist a few years ago?  This pa
 nel brings together experienced professionals from academia, the US nation
 al labs and industry to discuss the current state of storage for AI/ML, an
 d find the elusive AI/ML storage requirements based on their experiences t
 rying to use these systems to support AI/ML workloads.  The moderator is D
 ean Hildebrand (Google) and deputy moderator is Jay Lofstead (Sandia Natio
 nal Laboratories).\n\nTag: Storage\n\nRegistration Category: Tech Program 
 Reg Pass
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