Energy-Efficient Cluster Computing via Accurate Workload Characterization

Huang, Shi-Yu; Feng, Wu-chun

doi:10.1109/ccgrid.2009.88

Cited by 84 publications

(59 citation statements)

References 12 publications

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“…Studying the power consumption of large scale servers and data centers as a means to achieve energy-proportional computing is an active research area [3], [1], [17], [15]. Broadly speaking, the studies focus on one of the following aspects, namely, (1) on investigating the power consumption characteristics of a server as a whole or (2) on investigating the relationship between the power consumption and the workload of a server.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model for Estimating the Power Consumption of Processors and Network Interface Cards

Dargie

Wen

2013

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications

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Abstract-Many of the proposed mechanisms aiming to achieve energy-aware adaptations in server environments rely on the existence of models that estimate the power consumption of the server as well as its individual components. Most existing or proposed models employ performance (hardware) monitoring counters and the CPU utilization to estimate power consumption, but they do not take into account the statistics of the workload the server processes. In this paper we propose a lightweight probabilistic model that can be used to estimate the power consumption of the CPU, the network interface card (NIC), and the server as a whole. We tested the model's accuracy by executing custommade benchmarks as well as standard benchmarks on two heterogeneous server platforms. The estimation error associated with our model is less than 1% for the custom-made benchmark whereas it is less than 12% for the standard benchmark.

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Section: Introductionmentioning

confidence: 99%

A Probabilistic Model for Estimating the Power Consumption of Processors and Network Interface Cards

Dargie

Wen

2013

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications

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“…Modern processor architectures allow users to control the frequency of the chip through DVFS modules. There have been many studies on the use of DVFS for energy efficient computing for HPC workloads [24,34,40,44]. Rizvandi et al [38] make some observations on optimal frequency selection in DVFS-based energy consumption minimization.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient computing for HPC workloads on heterogeneous manycore chips

Langer

Totoni

Palekar

et al. 2015

Proceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores

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Power and energy efficiency is one of the major challenges to achieve exascale computing in the next several years. While chips operating at low voltages have been studied to be highly energy-efficient, low voltage operations lead to heterogeneity across cores within the microprocessor chip. In this work, we study chips with low voltage operation and discuss programming systems, and performance modeling in the presence of heterogeneity. We propose an integer linear programming based approach for selecting optimal configuration of a chip that minimizes its energy consumption. We obtain an average of 26% and 10.7% savings in energy consumption of the chip for two HPC mini-applications -miniMD and Jacobi, respectively. We also evaluate the energy savings with execution time constraints, using the proposed approach. These energy savings are significantly more than the savings by sub-optimal configurations obtained from heuristics.

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“…Time intervalbased approaches take observations about the application from previous intervals to estimate the time/power requirements and workload of upcoming intervals. These estimations are mostly based on hardware counters aggregated in the previous intervals: cache accesses counters [9], MIPS (Millions of Instructions per Second) [10] and CPU stall cycles [11]. Time interval-based approaches can run into suboptimal behavior when pre-defined time-intervals do not happen to line up with changes in application behavior.…”

Section: Related Workmentioning

confidence: 99%

Green Queue: Customized Large-Scale Clock Frequency Scaling

Tiwari

Laurenzano

Peraza

et al. 2012

2012 Second International Conference on Cloud and Green Computing

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Abstract-We examine the scalability of a set of techniques related to Dynamic Voltage-Frequency Scaling (DVFS) on HPC systems to reduce the energy consumption of scientific applications through an application-aware analysis and runtime framework, Green Queue. Green Queue supports making CPU clock frequency changes in response to intra-node and internode observations about application behavior. Our intra-node approach reduces CPU clock frequencies and therefore power consumption while CPUs lacks computational work due to inefficient data movement. Our inter-node approach reduces clock frequencies for MPI ranks that lack computational work.We investigate these techniques on a set of large scientific applications on 1024 cores of Gordon, an Intel Sandybridgebased supercomputer at the San Diego Supercomputer Center. Our optimal intra-node technique showed an average measured energy savings of 10.6% and a maximum of 21.0% over regular application runs. Our optimal inter-node technique showed an average 17.4% and a maximum of 31.7% energy savings.

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Energy-Efficient Cluster Computing via Accurate Workload Characterization

Abstract: Abstract

Cited by 84 publications

References 12 publications

A Probabilistic Model for Estimating the Power Consumption of Processors and Network Interface Cards

A Probabilistic Model for Estimating the Power Consumption of Processors and Network Interface Cards

Energy-efficient computing for HPC workloads on heterogeneous manycore chips

Green Queue: Customized Large-Scale Clock Frequency Scaling

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