Optimizing pipelines for power and performance

Srinivasan, Vijayalakshmi; Brooks, David; Gschwind, Michael; Bose, Pradip; Zyuban, Victor; Strenski, Philip N.; Emma, Philip

doi:10.1109/micro.2002.1176261

Cited by 91 publications

(78 citation statements)

References 29 publications

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“…They argue that Et 2 provides the best balance between the two optimisation objectives for microprocessor design. Srinivasan et al reached the same conclusion, although for slightly different reasons [22].…”

Section: Related Workmentioning

confidence: 78%

Metrics for Energy-Aware Software Optimisation

Roberts

Wright

Fahmy

et al. 2017

Lecture Notes in Computer Science

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A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk Metrics for Energy-Aware Software OptimisationStephen I. Roberts, Steven A. Wright, Suhaib A. Fahmy, and Stephen A. Jarvis University of Warwick, U.K. S.I.Roberts@warwick.ac.uk Abstract. Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community are often re-purposed to guide these software optimisation efforts. In this paper we argue that established metrics, and in particular those in the Energy Delay Product (Et n ) family, are unsuitable for energy-aware software optimisation. A good metric should provide meaningful values for a single experiment, allow fair comparison between experiments, and drive optimisation in a sensible direction. We show that Et n metrics are unable to fulfil these basic requirements and present suitable alternatives for guiding energy-aware software optimisation. We finish with a practical demonstration of the utility of our proposed metrics.

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Section: Related Workmentioning

confidence: 78%

Metrics for Energy-Aware Software Optimisation

Roberts

Wright

Fahmy

et al. 2017

Lecture Notes in Computer Science

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“…On the other hand, internal architectural counters used to compute the power consumed by cores count a number of relevant events and appropriately weight the counter values. For example, the total numbers of load/store instructions, arithmetic/logic instructions, floating-point operations, and retirement executions for each core are counted and summed up after being multiplied by appropriate weights [47]. The decision boundaries for an output measure are obtained as follows.…”

Section: B Detailed Resultsmentioning

confidence: 99%

Supervised Learning Based Power Management for Multicore Processors

Jung

Pedram

2010

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

107

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-This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the occupancy state of a global service queue) and then uses this predicted state to look up the optimal power management action (e.g., voltage-frequency setting) from a precomputed policy table. The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce the overhead of the PM which has to repetitively determine and assign voltage-frequency settings for each processor core in the system. Experimental results demonstrate that the proposed supervised learning based power management technique ensures system-wide energy savings under rapidly and widely varying workloads.Index Terms -Bayesian classification, dynamic power management, machine learning, multi-processor system, supervised learning

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“…To model the power overheads of deep pipelining, we employ the analytical models proposed by Srinivasan et al [38] to compute power-optimal pipeline depths. More details of this model and assumptions can be found in the original work [38]. Table 5 summarizes the impact of pipeline depth on total power, relative to a baseline pipeline that has a cycle time of 18 FO4.…”

Section: Conservative Timing Marginsmentioning

confidence: 99%