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DTSTART;TZID=America/Chicago:20211116T103000
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UID:submissions.supercomputing.org_SC21_sess149_pap483@linklings.com
SUMMARY:Generalizable Coordination of Large Multiscale Ensembles: Challeng
 es and Learnings at Scale
DESCRIPTION:Paper\n\nGeneralizable Coordination of Large Multiscale Ensemb
 les: Challenges and Learnings at Scale\n\nBhatia, Di Natale, Moon, Zhang, 
 Chavez...\n\nThe advancement of machine learning techniques and the availa
 bility of heterogeneous computing are propelling the demand for large mult
 iscale simulations that can automatically and autonomously couple diverse 
 components to solve complex problems at multiple scales. Nevertheless, the
  current capabilities are limited to coupling two scales. \n\nIn the first
 -ever demonstration of using three resolution scales, we present a scalabl
 e and generalizable framework as we expand MuMMI, an award-winning workflo
 w, beyond its original design. We discuss the challenges and learnings in 
 executing a massive simulation campaign that utilized over 600,000 node-ho
 urs on Summit, achieving more than 98% GPU occupancy for over 83% of the t
 ime. We enable orders of magnitude scaling, including coordinating 24,000 
 jobs, and managing several TBs of new data per day and over a billion file
 s in total. Finally, we describe the generalizability of our framework and
  discuss how the presented framework may be used for new applications.\n\n
 Tag: Machine Learning and Artificial Intelligence, Resource Management and
  Scheduling, State of the Practice\n\nRegistration Category: Tech Program 
 Reg Pass
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