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Scientific Programming
Volume 22, Issue 2, Pages 59-74
http://dx.doi.org/10.3233/SPR-140387

Enabling Fair Pricing on High Performance Computer Systems with Node Sharing

Alex D. Breslow,1 Ananta Tiwari,2 Martin Schulz,3 Laura Carrington,2 Lingjia Tang,4 and Jason Mars4

1University of California, San Diego, CA, USA
2San Diego Supercomputer Center, La Jolla, CA, USA
3Lawrence Livermore National Laboratory, Livermore, CA, USA
4University of Michigan, Ann Arbor, MI, USA

Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.