Energy-aware metrics for benchmarking heterogeneous systems

McIntosh–Smith, Simon; Wilson, Terry; Crisp, Jon; Ibarra, Amaurys Ávila; Sessions, Richard B.

doi:10.1145/1964218.1964233

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…Following the trend reported elsewhere [5,9], the OpenCL performance is marginally better (1.17x) than the FORTRAN 77 implementation compiled with GCC. When compiled with Sun Studio, the FORTRAN 77 code is 1.5x faster than OpenCL.…”

Section: Performance Of Opencl Vs Native Implementationssupporting

confidence: 73%

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An investigation of the performance portability of OpenCL

Pennycook

Hammond

Wright

et al. 2013

Journal of Parallel and Distributed Computing

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Section: Performance Of Opencl Vs Native Implementationssupporting

confidence: 73%

“…Other work has investigated the use of OpenCL on different platforms as a means of assessing power usage [5] and productivity [6].…”

Section: Related Workmentioning

confidence: 99%

An investigation of the performance portability of OpenCL

Pennycook

Hammond

Wright

et al. 2013

Journal of Parallel and Distributed Computing

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“…This interest increases the relevance of concepts like energy costs and sustainability. Specifically, some research software developers focus on energy efficiency and carbon emissions [11].…”

Section: A Metricsmentioning

confidence: 99%

A Survey of Software Metric Use in Research Software Development

Eisty

Thiruvathukal

Carver

2018

2018 IEEE 14th International Conference on E-Science (E-Science)

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Background: Breakthroughs in research increasingly depend on complex software libraries, tools, and applications aimed at supporting specific science, engineering, business, or humanities disciplines. The complexity and criticality of this software motivate the need for ensuring quality and reliability. Software metrics are a key tool for assessing, measuring, and understanding software quality and reliability. Aims: The goal of this work is to better understand how research software developers use traditional software engineering concepts, like metrics, to support and evaluate both the software and the software development process. One key aspect of this goal is to identify how the set of metrics relevant to research software corresponds to the metrics commonly used in traditional software engineering. Method: We surveyed research software developers to gather information about their knowledge and use of code metrics and software process metrics. We also analyzed the influence of demographics (project size, development role, and development stage) on these metrics. Results: The survey results, from 129 respondents, indicate that respondents have a general knowledge of metrics. However, their knowledge of specific SE metrics is lacking, their use even more limited. The most used metrics relate to performance and testing. Even though code complexity often poses a significant challenge to research software development, respondents did not indicate much use of code metrics. Conclusions: Research software developers appear to be interested and see some value in software metrics but may be encountering roadblocks when trying to use them. Further study is needed to determine the extent to which these metrics could provide value in continuous process improvement.

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“…Some recent work in energy‐efficient cloud computing benefits from virtualization capabilities, such as load balancing, resource allocation, and virtual machine scheduling, to make the environment more sustainable . Another study proposes power models to quantify the energy consumption of different workloads . Beloglazov and Buyya balance the service‐level agreement (SLA) metrics and energy constraints, describing the energy consumption through a linear model.…”

Section: Background and State Of The Artmentioning

confidence: 99%

Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

Xavier

Rossi

Rose

et al. 2016

Concurrency and Computation

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SUMMARYThe more large-scale data centers infrastructure costs increase, the more simulation-based evaluations are needed to understand better the trade-off between energy and performance and support the development of new energy-aware resource allocation policies. Specifically, in the cloud computing field, various simulators are able to predict and measure the behavior of applications on different architectures using different resource allocation policies. Yet, only a few of them have the ability to simulate energy-saving strategies, and none of them support the complete advanced configuration and power interface (ACPI) specification. ACPI defines a terminology for all possible power states of a machine and their associated power rate. The hardware industry has relied on ACPI to provide up-to-date standard interfaces for hardware discovery, configuration, power management, and monitoring, enabling a better understanding of the energy consumption level of different hardware states, referred to as ACPI G-states, S-states, and P-states. In this paper, we improve the modeling and simulation of the ACPI G/S-states and show not only that these states offer different energy-saving levels but also that state transitions consume energy. In addition, we model the latency to transit between two states and the effects on the turnaround time when the transitions are not performed conservatively. Furthermore, the equations provide essential information to quantify the trade-off between energy consumption and performance and assist in the analysis/decision on which strategy fits better in the environment and how it could be refined. Our expanded energy model was implemented in CloudSim and validated with simulation-based experiments with a very high level of accuracy, with a standard deviation of at most 6%.

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Energy-aware metrics for benchmarking heterogeneous systems

Cited by 5 publications

References 12 publications

An investigation of the performance portability of OpenCL

An investigation of the performance portability of OpenCL

A Survey of Software Metric Use in Research Software Development

Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

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