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DTSTAMP:20211207T054801Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211116T163000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess145_pap336@linklings.com
SUMMARY:Systematically Inferring I/O Performance Variability by Examining 
 Repetitive Job Behavior
DESCRIPTION:Paper\n\nSystematically Inferring I/O Performance Variability 
 by Examining Repetitive Job Behavior\n\nCosta, Patel, Schwaller, Brandt, T
 iwari\n\nMonitoring and analyzing I/O behaviors is critical to the efficie
 nt utilization of parallel storage systems. Unfortunately, with increasing
  I/O requirements and resource contention, I/O performance variability is 
 becoming a significant concern.  This paper investigates I/O behavior and 
 performance variability on a large-scale high-performance computing (HPC) 
 system using a novel methodology that identifies similarity across jobs fr
 om the same application leveraging an I/O characterization tool and then, 
 detects potential I/O performance variability across jobs of the same appl
 ication. We demonstrate and discuss how our unique methodology can be used
  to perform temporal and feature analyses to detect interesting I/O perfor
 mance variability patterns in production HPC systems, and their implicatio
 ns for operating/managing large-scale systems.\n\nTag: Reproducibility Bad
 ge, State of the Practice\n\nRegistration Category: Tech Program Reg Pass\
 n\nReproducibility Badges: Artifact Available, Artifact Functional, Result
 s Reproduced
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