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DTSTAMP:20260522T150119Z
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:Maxime Martinasso, Miguel Gila, Mauro Bianco, Sadaf R. Alam, C
 olin McMurtrie, and Thomas C. Schulthess (Swiss National Supercomputing Ce
 ntre)\n\nLeading hybrid and heterogeneous supercomputing systems process h
 undreds of thousands of jobs using complex scheduling algorithms and param
 eters. The centers operating these systems aim to achieve higher levels of
  resource utilization while being restricted by compliance with policy con
 straints. There is a critical need for a high-fidelity, high-performance t
 ool with familiar interfaces that allows not only tuning and optimization 
 of the operational job scheduler but also enables exploration of new resou
 rce management algorithms. We propose a new methodology and a tool called 
 RM-Replay which is not a simulator but instead a fast replay engine for pr
 oduction workloads. Slurm is used as a platform to demonstrate the capabil
 ities of our replay engine.\n\nThe tool accuracy is discussed and our inve
 stigation shows that, by providing better job runtime estimation or using 
 topology-aware allocation, scheduling metric values vary. The presented me
 thodology to create fast replay engines can be extended to other complex s
 ystems.\n\nTag: Networks, Resource Management, Scheduling, State of the Pr
 actice, System Software\n\nRegistration Category: Tech Program Reg Pass\n\
 nFinalist: BSP Finalist\n\nSession Chair: Bronis R. de Supinski (Lawrence 
 Livermore National Laboratory (LLNL), Queens University Belfast (QUB))\n\n
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