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DTSTART;TZID=America/Chicago:20181113T160000
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UID:submissions.supercomputing.org_SC18_sess274_exforum108@linklings.com
SUMMARY:Analytic Based Monitoring of High Performance Computing Applicatio
 ns
DESCRIPTION:William Leinberger (General Dynamics Mission Systems)\n\nThe c
 omplexity of High Performance Computing (HPC) systems and the innate premi
 um on system efficiency necessitate the use of automated tools to monitor 
 not only system-level health and status, but also job performance. Current
  vendor-provided and third party monitoring tools, such as Nagios or Gangl
 ia, enable system-level monitoring using features that reflect the state o
 f system resources. None of those tools, however, are designed to determin
 e the health and status of a user’s application, or job, as it executes.  
 \n\nThis presentation introduces the concept of job-level, analytics-based
  monitoring using system features external to the job, like those reported
  by Ganglia. Preliminary results show these features contain sufficient in
 formation content to characterize key behaviors of an executing job when i
 ncorporated into a job-specific, application-neutral analytic model; trans
 itions between computational phases, onset of a load imbalance, and anomal
 ous activity on a compute node may each be detected using this approach.\n
 \nTag: Data Analytics, Debugging, Parallel Programming Languages, Librarie
 s, and Models, Software Engineering, System Software\n\nSession Chair: Sco
 tt Michael (Indiana University)\n\n
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