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DTSTAMP:20260202T201804Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251118T080000
DTEND;TZID=America/Chicago:20251118T170000
UID:submissions.supercomputing.org_SC25_sess527_post215@linklings.com
SUMMARY:Classifying Performance Bounds Using Machine Learning
DESCRIPTION:Lewis Littman and Tom Deakin (University of Bristol)\n\nTradit
 ional performance analysis tools, such as the roofline model, require visu
 al interpretation to determine performance bounds. For CPUs which have com
 plex cache hierarchies and front-end out-of-order capabilities—that is, th
 e CPUs we use for high performance computing—accurately identifying the tr
 ue performance bound is challenging. This work is the first step towards a
  data-driven approach to performance modeling, leveraging machine learning
  techniques. We build and evaluate a number of supervised and unsupervised
  models using a new curated data set of performance counters collected fro
 m well-understood (i.e., easily labeled) benchmark applications. We furthe
 r analyze the data set and highlight potential "performance fingerprints" 
 obtainable using this methodology.\n\nTag: Research & ACM SRC Posters\n\nR
 egistration Category: Technical Program Reg Pass\n\nSession Chairs: Kento 
 Sato (RIKEN Center for Computational Science (R-CCS)); Chris Schlipalius (
 Pawsey Supercomputing Research Centre; Commonwealth Scientific and Industr
 ial Research Organisation (CSIRO), Australia); and Anja Gerbes (Georg-Augu
 st-Universität Göttingen)\n\n
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