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UID:submissions.supercomputing.org_SC24_sess533_post262@linklings.com
SUMMARY:Enhancing Performance Reproducibility on HPC Workflows
DESCRIPTION:Ainara Garcia (Clemson University, Argonne National Laboratory
  (ANL))\n\nReproducibility is related to achieving consistent performance 
 across multiple runs of the same application in an identical computing env
 ironment. From a computational perspective, jobs should run for equal time
 , with equal performance repeatedly. Without this, we see significant vari
 ations in performance, which can undermine the reliability of scientific r
 esults. However, the complexity and scale of these workflows present uniqu
 e challenges, especially when it comes to achieving consistent performance
  across repeated runs. We seek to provide researchers with Findable, Acces
 sible, Interoperable and Reusable (FAIR) data. Ensuring the “FAIRness” of 
 (meta)data can reduce barriers to reproducibility by making this informati
 on easier to find and interpret, programmatically access, and reuse in new
  contexts. Therefore we are exploring the process of analyzing performance
  data and seek to integrate our findings in the RECUP framework for reprod
 ucibility, showing data sources, repository saving intermediate results, a
 nd user analysis of performance and result reproducibility.\n\nRegistratio
 n Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ay
 esha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen N
 ational High Performance Computing Center); Sally Ellingson (University of
  Kentucky); and Alan Sussman (University of Maryland)\n\n
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