SCHEDULE: NOV 16-22, 2013

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Enabling Fair Pricing on HPC Systems with Node Sharing

SESSION: Performance Management of HPC Systems

EVENT TYPE: Papers, Awards, Best Paper Finalists, Best Student Paper Finalists

TIME: 10:30AM - 11:00AM

SESSION CHAIR: Sadaf R. Alam

AUTHOR(S):Alex D. Breslow, Ananta Tiwari, Martin Schulz, Laura Carrington, Lingjia Tang, Jason Mars

ROOM:401/402/403

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 to 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 benchmarks and real scientific workloads.

Chair/Author Details:

Sadaf R. Alam (Chair) - Swiss National Supercomputing Centre

Alex D. Breslow - University of California, San Diego

Ananta Tiwari - San Diego Supercomputer Center

Martin Schulz - Lawrence Livermore National Laboratory

Laura Carrington - San Diego Supercomputer Center

Lingjia Tang - University of Michigan

Jason Mars - University of Michigan

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The full paper can be found in the ACM Digital Library