Dynamic adaptive virtual core mapping to improve power, energy, and performance in multi-socket multicores

  • Authors:

  • Chang S. Bae;Lei Xia;Peter Dinda;John Lange

  • Affiliations:

  • Northwestern University, Evanston, IL, USA;Northwestern University, Evanston, IL, USA;Northwestern University, Evanston, IL, USA;University of Pittsburgh, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
  • Year:
  • 2012

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Abstract

Consider a multithreaded parallel application running inside a multicore virtual machine context that is itself hosted on a multi-socket multicore physical machine. How should the VMM map virtual cores to physical cores? We compare a local mapping, which compacts virtual cores to processor sockets, and an interleaved mapping, which spreads them over the sockets. Simply choosing between these two mappings exposes clear tradeoffs between performance, energy, and power. We then describe the design, implementation, and evaluation of a system that automatically and dynamically chooses between the two mappings. The system consists of a set of efficient online VMM-based mechanisms and policies that (a) capture the relevant characteristics of memory reference behavior, (b) provide a policy and mechanism for configuring the mapping of virtual machine cores to physical cores that optimizes for power, energy, or performance, and (c) drive dynamic migrations of virtual cores among local physical cores based on the workload and the currently specified objective. Using these techniques we demonstrate that the performance of SPEC and PARSEC benchmarks can be increased by as much as 66%, energy reduced by as much as 31%, and power reduced by as much as 17%, depending on the optimization objective.