Adaptive, transparent CPU scaling algorithms leveraging inter-node MPI communication regions

Min Yeol Lim, Vincent W. Freeh, David K. Lowenthal

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Although users of high-performance computing are most interested in raw performance, both energy and power consumption have become critical concerns. Because the CPU is often the major power consumer, some microprocessors allow frequency and voltage scaling, which enables a system to efficiently reduce CPU performance and power. When the CPU is not on the critical path, such dynamic frequency and voltage scaling can produce significant energy savings with little performance penalty. This paper presents an MPI runtime system that dynamically reduces CPU frequency and voltage during communication phases in MPI programs. It dynamically identifies such phases and, without a priori knowledge, selects the CPU frequency in order to minimize energy-delay product. All analysis and subsequent frequency and voltage scaling is within MPI and so is entirely transparent to the application. This means that the large number of existing MPI programs, as well as new ones being developed, can use our system without modification. Results show that the median reduction in energy-delay product for twelve benchmarks is 8%, the median energy reduction is 11%, and the median increase in execution time increase is only 2%.

Original languageEnglish (US)
Pages (from-to)667-683
Number of pages17
JournalParallel Computing
Issue number10-11
StatePublished - Oct 2011


  • Message passing interface (MPI)
  • Power-aware computing

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


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