Simulation of power consumption of energy efficient cluster hardware

Minartz, Timo; Kunkel, Julian; Ludwig, Thomas

doi:10.1007/s00450-010-0120-6

Cited by 21 publications

(14 citation statements)

References 16 publications

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“…average achieved performance average HPC system power used 10 on the Top500 List -Nov 2013). As can be seen, the WPE measurement shows a lower efficiency rating then the Green500 results would indicate.…”

Section: Wpe =mentioning

confidence: 99%

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DWPE, a new data center energy-efficiency metric bridging the gap between infrastructure and workload

Wilde

Auweter

Patterson

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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To determine whether a High-Performance Computing (HPC) data center is energy efficient, various aspects have to be taken into account: the data center's power distribution and cooling infrastructure, the HPC system itself, the influence of the system management software, and the HPC workloads; all can contribute to the overall energy efficiency of the data center.Currently, two well-established metrics are used to determine energy efficiency for HPC data centers and systems: Power Usage Effectiveness (PUE) and FLOPS per Watt (as defined by the Green500 in their ranking list). PUE evaluates the overhead for running a data center and FLOPS per Watt characterizes the energy efficiency of a system running the High-Performance Linpack (HPL) benchmark, i.e. floating point operations per second achieved with 1 watt of electrical power. Unfortunately, under closer examination even the combination of both metrics does not characterize the overall energy efficiency of a HPC data center.First, HPL does not constitute a representative workload for most of today's HPC applications and the rev 0.9 Green500 run rules for power measurements allows for excluding subsystems (e.g. networking, storage, cooling). Second, even a combination of PUE with FLOPS per Watt metric neglects that the total energy efficiency of a system can vary with the characteristics of the data center in which it is operated. This is due to different cooling technologies implemented in HPC systems and the difference in costs incurred by the data center removing the heat using these technologies.To address these issues, this paper introduces the metrics system PUE (sPUE) and Data center Workload Power Efficiency (DWPE). sPUE calculates the overhead for operating a given system in a certain data center. DWPE is then calculated by determining the energy efficiency of a specific workload and dividing it by the sPUE. DWPE can then be used to define the energy efficiency of running a given workload on a specific HPC system in a specific data center and is currently the only fully-integrated metric suitable for rating an HPC data center's energy efficiency. In addition, DWPE allows for predicting the energy efficiency of different HPC systems in existing HPC data centers, thus making it an ideal approach for guiding HPC system procurement. This paper concludes with a demonstration of the application of DWPE using a set of representative HPC workloads.

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Section: Wpe =mentioning

confidence: 99%

“…Energy-to-Solution (EtS) as defined in [10] is another metric for measuring the energy efficiency of an HPC system for a specific workload. It shows the energy consumed by the HPC system for solving a specific problem using a specific application (equation 3).…”

Section: Pue =mentioning

confidence: 99%

DWPE, a new data center energy-efficiency metric bridging the gap between infrastructure and workload

Wilde

Auweter

Patterson

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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“…Minartz et al [12] explore energy saving through the dynamic provisioning of resources within a high performance computing cluster, while Niemi et al [13] demonstrate energy savings through the consolidation of multiple jobs onto the same hardware. Ponciano et al evaluate strategies for energy-aware resource provisioning and job allocation within opportunistic grids [14].…”

Section: B Energy Efficient High Throughput Computingmentioning

confidence: 99%

Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems

Forshaw

Thomas

McGough

2014

2014 IEEE/ACM 18th International Symposium on Distributed Simulation and Real Time Applications

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any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-High Throughput Computing (HTC) is a powerful paradigm allowing vast quantities of independent work to be performed simultaneously. However, until recently little evaluation has been performed on the energy impact of HTC. Many organisations now seek to minimise energy consumption across their IT infrastructure though it is unclear how this will affect the usability of HTC systems. We present here HTC-Sim, a simulation system which allows the evaluation of different energy reduction policies across an HTC system comprising a collection of computational resources dedicated to HTC work and resources provided through cycle scavenging -a Desktop Grid. We demonstrate that our simulation software scales linearly with increasing HTC workload.

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“…In [15], they present a simulator which replays recorded utilization traces of applications and uses a simplified power model based on utilization metrics to estimate the performance. Another example of model application is presented in [13] with vEC ("Virtual energy counters").…”

Section: Related Workmentioning

confidence: 99%

An analytical methodology to derive power models based on hardware and software metrics

et al. 2015

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The use of models to predict the power consumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present a systematic methodology to build models with a reduced number of features in order to estimate power consumption at node level. We aim at building simple power models by performing a per-component analysis (CPU, memory, network, I/O) through the execution of four standard benchmarks. While they are executed, we collect information from all the available hardware counters and resource utilization metrics provided by the system. Based on correlations among the recorded metrics and their correlation with the instantaneous power, our methodology allows i) to identify the significant metrics; and ii) to assign weights to the selected metrics in order to derive reduced models. The reduction also aims at extracting models that are based on a set of hardware counters and utilization metrics that can be obtained simultaneously and, thus, can be gathered and computed on-line. The utility of our procedure is validated using real-life applications on an Intel Sandy Bridge architecture.

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Simulation of power consumption of energy efficient cluster hardware

Cited by 21 publications

References 16 publications

DWPE, a new data center energy-efficiency metric bridging the gap between infrastructure and workload

DWPE, a new data center energy-efficiency metric bridging the gap between infrastructure and workload

Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems

An analytical methodology to derive power models based on hardware and software metrics

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