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Exploiting Workload Parallelism for Performance and Power Optimization in Blue Gene
September/October 2006 (vol. 26 no. 5)
pp. 67-81
Valentina Salapura, IBM T.J. Watson Research Center
Robert Walkup, IBM T.J. Watson Research Center
Alan Gara, IBM T.J. Watson Research Center
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.
Index Terms:
computer systems organization, application studies resulting in better multiple-processor systems, super (very large) computers, computer system implementation, architecture, interprocessor communications, processor architectures, parallelism, parallelism, Blue Gene/L system, power optimization
Citation:
Valentina Salapura, Robert Walkup, Alan Gara, "Exploiting Workload Parallelism for Performance and Power Optimization in Blue Gene," IEEE Micro, vol. 26, no. 5, pp. 67-81, Sept.-Oct. 2006, doi:10.1109/MM.2006.89
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