Enabling fair pricing on high performance computer systems with node sharing

Alex D. Breslow, Ananta Tiwari, Martin Schulz, Laura Carrington, Lingjia Tang, Jason Mars

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Co-location, where multiple jobs share compute nodes in large-scale HPC systems, has been shown to increase aggregate throughput and energy efficiency by 10-20%. However, system operators disallow co-location due to fair-pricing concerns, i.e., a pricing mechanism that considers performance interference from co-running jobs. In the current pricing model, application execution time determines the price, which results in unfair prices paid by the minority of users whose jobs suffer from co-location. This paper presents POPPA, a runtime system that enables fair pricing by delivering precise online interference detection and facilitates the adoption of supercomputers with co-locations. POPPA leverages a novel shutter mechanism-a cyclic, fine-grained interference sampling mechanism to accurately deduce the interference between co-runners-to provide unbiased pricing of jobs that share nodes. POPPA is able to quantify inter-application interference within 4% mean absolute error on a variety of co-located benchmark and real scientific workloads.

Original languageEnglish
Pages (from-to)59-74
Number of pages16
JournalScientific Programming
Volume22
Issue number2
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Online pricing
  • chip multiprocessor
  • computer systems management
  • contention
  • resource sharing
  • supercomputer accounting

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