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DTSTAMP:20260422T143141Z
LOCATION:B311
DTSTART;TZID=America/New_York:20241118T094000
DTEND;TZID=America/New_York:20241118T095700
UID:submissions.supercomputing.org_SC24_sess755_ws_prot109@linklings.com
SUMMARY:An Incremental Multi-Level, Multi-Scale Approach to Assessment of 
 Multifidelity HPC Systems
DESCRIPTION:Shilpika Shilpika, Bethany Lusch, and Venkatram Vishwanath (Ar
 gonne National Laboratory (ANL)) and Michael Papka (Argonne National Labor
 atory (ANL), University of Illinois Chicago)\n\nWith the growing complexit
 y in architecture and the size of large-scale computing systems, monitorin
 g and analyzing system behavior and events has become daunting. Monitoring
  data amounting to terabytes per day are collected by sensors housed in th
 ese massive systems at multiple fidelity levels and varying temporal resol
 utions. In this work, we develop an incremental version of multiresolution
  dynamic mode decomposition (mrDMD), which converts high-dimensional data 
 to spatial-temporal patterns at varied frequency ranges. Our incremental i
 mplementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable i
 nformation in the massive environment log dataset, which is then visually 
 aligned with the processed hardware and job log datasets through our gener
 alizable rack visualization using D3 visualization integrated into the Jup
 yter Notebook interface. We demonstrate the efficacy of our approach with 
 two use scenarios on a real-world dataset from a Cray XC40 supercomputer, 
 Theta.\n\nTag: Debugging and Correctness Tools, Performance Evaluation and
 /or Optimization Tools\n\nRegistration Category: Workshop Reg Pass\n\nSess
 ion Chairs: David Boehme (Lawrence Livermore National Laboratory (LLNL)); 
 Kate Isaacs (University of Utah); Carmen Navarrete (Leibniz Supercomputing
  Centre (LRZ)); and Emmanuelle Saillard (CEA, DAM, DIF)\n\n
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