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DTSTART;TZID=America/Denver:20231112T091900
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UID:submissions.supercomputing.org_SC23_sess422_ws_hpcsysp104@linklings.co
 m
SUMMARY:Embracing Batch on Kubernetes
DESCRIPTION:Jason Kincl and Patrick Bruszewski (Red Hat Inc)\n\nAs the ado
 ption of Kubernetes continues to grow, there is an increasing demand for p
 erforming larger scale batch processing using Kubernetes. Much of the init
 ial workloads are around machine learning but there is also interest in co
 nverging traditional HPC and Kubernetes clusters for operational efficienc
 ies.<br /><br />In this talk, we want to look at how we can leverage both 
 traditional HPC workload partitioning as well as features of Kubernetes to
  achieve a hybrid system that can be used for all types of workloads. We w
 ill show how we isolate the orchestration and user processes in Kubernetes
  allowing for maximum use of the hardware for running batch workloads with
  benchmark comparisons against a traditional HPC cluster.<br /><br />It is
  important to note that this area of research is still in its early stages
  and through our exploration of this topic we hope that this will continue
  to foster discussion in the HPC community.\n\nTag: Artificial Intelligenc
 e/Machine Learning, Cloud Computing, Distributed Computing, Data Analysis,
  Visualization, and Storage, Data Movement and Memory, Fault Handling and 
 Tolerance, I/O and File Systems, Large Scale Systems, Performance Optimiza
 tion, Resource Management, Security, State of the Practice\n\nRegistration
  Category: Workshop Reg Pass\n\nSession Chairs: Matt Bidwell (National Ren
 ewable Energy Laboratory (NREL)) and John Blaas (National Center for Atmos
 pheric Research (NCAR))\n\n
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