An effective speedup metric for measuring productivity in large-scale parallel computer systems

  • Authors:

  • Xuejun Yang;Jing Du;Zhiyuan Wang

  • Affiliations:

  • PDL, School of Computer, National University of Defense Technology, Changsha, China 410073;PDL, School of Computer, National University of Defense Technology, Changsha, China 410073;PDL, School of Computer, National University of Defense Technology, Changsha, China 410073

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2011

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Abstract

With the parallel computer systems scaling-up, the measure index for performance of the systems demands a shift from traditional "high performance" to "high productivity." This brings a new challenge to defining a synthetic, yet meaningful, measure index of multiple productivity variables; namely computing performance, reliability, energy consumption, parallel software development, etc. Traditional measures for large-scale parallel computer systems merely focus on computing performance, and are incapable of measuring the multiple productivity variables simultaneously in an effective manner. A recently proposed market-related money model, which pursues high utility/cost ratio, relies on money as a measure to consider the multiple productivity variables. Differing from the previous models, this paper proposes a novel system productivity speedup metric for large-scale parallel computer systems. The metric uses speedup instead of money to comprehensively unify the measures of multiple productivity variables. Finally, we propose a trade-off productivity measurement to weigh different productivity variables, to address different design targets. The measurement can facilitate the system evaluation, expose future technique tendencies, and guide future system design.