Finding Stress Patterns in MicroprocessorWorkloads

Abstract : Power consumption has emerged as a key design concern across the entire computing range, from low-end embedded systems to high-end supercomputers. Understanding the power characteristics of a microprocessor under design requires a careful study using a variety of workloads. These workloads range from benchmarks that represent typical behavior up to hand-tuned stress benchmarks (so called stressmarks) that stress the microprocessor to its extreme power consumption. This paper closes the gap between these two extremes by studying techniques for the automated identification of stress patterns (worst-case application behaviors) in typical workloads. For doing so, we borrow from sampled simulation theory and we provide two key insights. First, although representative sampling is slightly less effective in characterizing average behavior than statistical sampling, it is substantially more effective in finding stress patterns. Second, we find that threshold clustering is a better alternative than k-means clustering, which is typically used in representative sampling, for finding stress patterns. Overall, we can identify extreme energy and power behaviors in microprocessor workloads with a three orders of magnitude speedup with an error of a few percent on average.
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Communication dans un congrès
André Seznec and Joel Emer and Mike O'Boyle and Margaret Martonosi and Theo Ungerer. HiPEAC 2009 - High Performance and Embedded Architectures and Compilers, Jan 2009, Paphos, Cyprus. Springer, 2009, 〈10.1007/978-3-540-92990-1_13〉
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https://hal.inria.fr/inria-00445938
Contributeur : Ist Rennes <>
Soumis le : lundi 11 janvier 2010 - 15:53:50
Dernière modification le : lundi 20 juin 2016 - 14:10:32

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Frederik Vandeputte, Lieven Eeckhout. Finding Stress Patterns in MicroprocessorWorkloads. André Seznec and Joel Emer and Mike O'Boyle and Margaret Martonosi and Theo Ungerer. HiPEAC 2009 - High Performance and Embedded Architectures and Compilers, Jan 2009, Paphos, Cyprus. Springer, 2009, 〈10.1007/978-3-540-92990-1_13〉. 〈inria-00445938〉

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