Adaptive configuration selection for power-constrained heterogeneous systems

Peter E. Bailey, David K Lowenthal, Vignesh Ravi, Barry Rountree, Martin Schulz, Bronis R. De Supinski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations


As power becomes an increasingly important design factor in high-end supercomputers, future systems will likely operate with power limitations significantly below their peak power specifications. These limitations will be enforced through a combination of software and hardware power policies, which will filter down from the system level to individual nodes. Hardware is already moving in this direction by providing power-capping interfaces to the user. The power/performance trade-off at the node level is critical in maximizing the performance of power-constrained cluster systems, but is also complex because of the many interacting architectural features and accelerators that comprise the hardware configuration of a node. The key to solving this challenge is an accurate power/performance model that will aid in selecting the right configuration from a large set of available configurations. In this paper, we present a novel approach to generate such a model offline using kernel clustering and multivariate linear regression. Our model requires only two iterations to select a configuration, which provides a significant advantage over exhaustive search-based strategies. We apply our model to predict power and performance for different applications using arbitrary configurations, and show that our model, when used with hardware frequency-limiting, selects configurations with significantly higher performance at a given power limit than those chosen by frequency-limiting alone. When applied to a set of 36 computational kernels from a range of applications, our model accurately predicts power and performance, it maintains 91% of optimal performance while meeting power constraints 88% of the time. When the model violates a power constraint, it exceeds the constraint by only 6% in the average case, while simultaneously achieving 54% more performance than an oracle.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Parallel Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
Publication statusPublished - Nov 13 2014
Event43rd International Conference on Parallel Processing, ICPP 2014 - Minneapolis, United States
Duration: Sep 9 2014Sep 12 2014


Other43rd International Conference on Parallel Processing, ICPP 2014
CountryUnited States



  • GPU APU power performance modeling power-constrained

ASJC Scopus subject areas

  • Software
  • Mathematics(all)
  • Hardware and Architecture

Cite this

Bailey, P. E., Lowenthal, D. K., Ravi, V., Rountree, B., Schulz, M., & De Supinski, B. R. (2014). Adaptive configuration selection for power-constrained heterogeneous systems. In Proceedings of the International Conference on Parallel Processing (November ed., Vol. 2014-November, pp. 371-380). [6957246] Institute of Electrical and Electronics Engineers Inc..