Studying the impact of application-level optimizations on the power consumption of multi-core architectures

  • Authors:
  • Shah Mohammad Faizur Rahman;Jichi Guo;Akshatha Bhat;Carlos Garcia;Majedul Haque Sujon;Qing Yi;Chunhua Liao;Daniel Quinlan

  • Affiliations:
  • University of Texas at San Antonio, San Antonio, TX, USA;University of Texas at San Antonio, San Antonio, TX, USA;University of Texas at San Antonio, San Antonio, TX, USA;University of Texas at San Antonio, San Antonio, TX, USA;University of Texas at San Antonio, San Antonio, TX, USA;University of Texas at San Antonio, San Antonio, TX, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA

  • Venue:
  • Proceedings of the 9th conference on Computing Frontiers
  • Year:
  • 2012

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Abstract

This paper studies the overall system power variations of two multi-core architectures, an 8-core Intel and a 32-core AMD workstation, while using these machines to execute a wide variety of sequential and multi-threaded benchmarks using varying compiler optimization settings and runtime configurations. Our extensive experimental study provides insights for answering two questions: 1) what degrees of impact can application level optimizations have on reducing the overall system power consumption of modern CMP architectures; and 2) what strategies can compilers and application developers adopt to achieve a balanced performance and power efficiency for applications from a variety of science and embedded systems domains.