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DTSTAMP:20181221T160729Z
LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181113T153000
DTEND;TZID=America/Chicago:20181113T160000
UID:submissions.supercomputing.org_SC18_sess182_pap360@linklings.com
SUMMARY:RM-Replay: A High-Fidelity Tuning, Optimization and Exploration To
 ol for Resource Management
DESCRIPTION:Paper\nNetworks, Resource Management, Scheduling, State of the
  Practice, System Software, Tech Program Reg Pass\n\nRM-Replay: A High-Fid
 elity Tuning, Optimization and Exploration Tool for Resource Management\n\
 nMartinasso, Gila, Bianco, Alam, McMurtrie...\n\nLeading hybrid and hetero
 geneous supercomputing systems process hundreds of thousands of jobs using
  complex scheduling algorithms and parameters. The centers operating these
  systems aim to achieve higher levels of resource utilization while being 
 restricted by compliance with policy constraints. There is a critical need
  for a high-fidelity, high-performance tool with familiar interfaces that 
 allows not only tuning and optimization of the operational job scheduler b
 ut also enables exploration of new resource management algorithms. We prop
 ose a new methodology and a tool called RM-Replay which is not a simulator
  but instead a fast replay engine for production workloads. Slurm is used 
 as a platform to demonstrate the capabilities of our replay engine.\n\nThe
  tool accuracy is discussed and our investigation shows that, by providing
  better job runtime estimation or using topology-aware allocation, schedul
 ing metric values vary. The presented methodology to create fast replay en
 gines can be extended to other complex systems.
URL:https://sc18.supercomputing.org/presentation/?id=pap360&sess=sess182
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