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UID:submissions.supercomputing.org_SC20_sess171_pap336@linklings.com
SUMMARY:Job Characteristics on Large-Scale Systems: Long-Term Analysis, Qu
 antification and Implications
DESCRIPTION:Paper\n\nJob Characteristics on Large-Scale Systems: Long-Term
  Analysis, Quantification and Implications\n\nPatel, Liu, Kettimuthu, Rich
 , Allcock...\n\nHPC workload analysis and resource consumption characteris
 tics are the key to driving better operation practices, system procurement
  decisions and designing effective resource management techniques. Unfortu
 nately, the HPC community does not have easy accessibility to long-term in
 trospective workload analysis and characterization for production-scale HP
 C systems. This study bridges this gap by providing detailed long-term qua
 ntification, characterization and analysis of job characteristics on two s
 upercomputers; Intrepid and Mira. This study is one of the largest of its 
 kind, covering trends and characteristics for over three billion compute h
 ours, 750 thousand jobs and spanning a decade. We confirm several long-hel
 d conventional wisdoms, and identify many previously undiscovered trends a
 nd their implications.  We also introduce a learning-based technique to pr
 edict the resource requirement of future jobs with high accuracy,  using f
 eatures available prior to job submission and without requiring  any appli
 cation-specific tracing or application-intrusive instrumentation.\n\nTag: 
 Data Analytics, Compression, and Management, Performance/Productivity Meas
 urement and Evaluation, Resource Management and Scheduling\n\nRegistration
  Category: Tech Program Reg Pass
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