Exploiting Workload Parallelism for Performance and Power Optimization in Blue Gene

  • Authors:
  • Valentina Salapura;Robert Walkup;Alan Gara

  • Affiliations:
  • IBM T.J. Watson Research Center;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center

  • Venue:
  • IEEE Micro
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Optimizing future supercomputing applications will depend on delivering the best performance for a given power budget. To determine the effect on efficiency of application-scaling parameters, this article analyzes system power and performance measurement results for real-world applications exploiting thread- and data-level parallelism on the Blue Gene/L system.