Enabling Fair Pricing on High Performance Computer Systems with Node Sharing

• Main Authors: Alex D. Breslow, Ananta Tiwari, Martin Schulz, Laura Carrington, Lingjia Tang, Jason Mars
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: Scientific Programming
• Online Access: http://dx.doi.org/10.3233/SPR-140387

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.