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DTSTART;TZID=America/Chicago:20251121T080000
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UID:submissions.supercomputing.org_SC25_sess620_post180@linklings.com
SUMMARY:Job Grouping-Based Intelligent Resource Recommendation Framework
DESCRIPTION:Beste Oztop (Boston University); Benjamin Schwaller, Vitus J. 
 Leung, and Jim Brandt (Sandia National Laboratories); and Brian Kulis, Man
 uel Egele, and Ayse K. Coskun (Boston University)\n\nIn the current large-
 scale computing systems, users from various scientific backgrounds submit 
 batch jobs with a set of requested resources. Manual resource selection in
  HPC facilities leads to early job terminations and out-of-memory errors d
 ue to underestimation of resources, or compute and memory resources sittin
 g idle because of overallocation. In this work, we provide a recommendatio
 n framework based on job grouping and intelligent prediction methods to pr
 ovision HPC application resource needs before they are submitted to the sy
 stem. Our work achieves less than 2\% of cases experiencing underpredicted
  resource requests, and results in fewer overestimations compared to the b
 aseline methods. We also implement a module to deploy the framework on a r
 eal HPC system, which comprises the future plans of this work.\n\nTag: Res
 earch & ACM SRC Posters\n\nRegistration Category: Technical Program Reg Pa
 ss\n\nSession Chairs: Kento Sato (RIKEN Center for Computational Science (
 R-CCS)); Anja Gerbes (Georg-August-Universität Göttingen); and Chris Schli
 palius (Pawsey Supercomputing Research Centre; Commonwealth Scientific and
  Industrial Research Organisation (CSIRO), Australia)\n\n
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