EPROF: An energy/performance/reliability optimization framework for streaming applications

Yetim, Yavuz; Malik, Sharad; Martonosi, Margaret

doi:10.1109/aspdac.2012.6165058

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…[34] considers a subset of stream programs that can be modeled as serial-parallel workflows, and studies the problem of mapping such workflows to CMPs to minimize energy. Eprof [35] designs an energy-efficient scheduling algorithm for stream applications, with a non-DVFS based solution. Rangasamy et.…”

Section: Related Workmentioning

confidence: 99%

Green Streams for data-intensive software

Bartenstein

Liu

2013

2013 35th International Conference on Software Engineering (ICSE)

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This paper introduces GREEN STREAMS, a novel solution to address a critical but often overlooked property of data-intensive software: energy efficiency. GREEN STREAMS is built around two key insights into data-intensive software. First, energy consumption of data-intensive software is strongly correlated to data volume and data processing, both of which are naturally abstracted in the stream programming paradigm; Second, energy efficiency can be improved if the data processing components of a stream program coordinate in a "balanced" way, much like an assembly line that runs most efficiently when participating workers coordinate their pace. GREEN STREAMS adopts a standard stream programming model, and applies Dynamic Voltage and Frequency Scaling (DVFS) to coordinate the pace of data processing among components, ultimately achieving energy efficiency without degrading performance in a parallel processing environment. At the core of GREEN STREAMS is a novel constraint-based inference to abstract the intrinsic relationships of data flow rates inside a stream program, that uses linear programming to minimize the frequencies -hence the energy consumption -for processing components while still maintaining the maximum output data flow rate. The core algorithm of GREEN STREAMS is formalized, and its optimality is established. The effectiveness of GREEN STREAMS is evaluated on top of the StreamIt framework, and preliminary results show the approach can save CPU energy by an average of 28% with a 7% performance improvement.

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Section: Related Workmentioning

confidence: 99%

Green Streams for data-intensive software

Bartenstein

Liu

2013

2013 35th International Conference on Software Engineering (ICSE)

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“…Different types of apps consume different power according to their usage of CPU cycles, network traffic and screen brightness, etc. Power consumption for an app can therefore be modeled by their resource usages [51], system call traces [50] and source code [17], etc.…”

Section: Power Characteristics Of Android Smartphonesmentioning

confidence: 99%

A Study on Power Side Channels on Mobile Devices

Lin

Guo

Chen

et al. 2015

Proceedings of the 7th Asia-Pacific Symposium on Internetware

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Power side channel is a very important category of side channels, which can be exploited to steal confidential information from a computing system by analyzing its power consumption. In this paper, we demonstrate the existence of various power side channels on popular mobile devices such as smartphones. Based on unprivileged power consumption traces, we present a list of real-world attacks that can be initiated to identify running apps, infer sensitive UIs, guess password lengths, and estimate geo-locations. These attack examples demonstrate that power consumption traces can be used as a practical side channel to gain various confidential information of mobile apps running on smartphones. Based on these power side channels, we discuss possible exploitations and present a general approach to exploit a power side channel on an Android smartphone, which demonstrates that power side channels pose imminent threats to the security and privacy of mobile users. We also discuss possible countermeasures to mitigate the threats of power side channels.

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“…Rafiev et al [2014] studied the interplay among time, energy costs, and reliability for a single-core and a multicore system, respectively, while they focused on concurrency and did not quantitatively elaborate the impacts of frequency/voltage on performance and reliability. Yetim et al [2012] presented an energy optimization framework using mixed-integer linear programming while meeting performance and reliability constraints. Targeting the application domain of multimedia, this work exploited the workload characteristics that limited error tolerance can be traded off for energy reduction.…”

Section: Related Workmentioning

confidence: 99%

Scalable Energy Efficiency with Resilience for High Performance Computing Systems

Tan

Song

2015

ACM Trans. Archit. Code Optim.

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Ever-growing performance of supercomputers nowadays brings demanding requirements of energy efficiency and resilience, due to rapidly expanding size and duration in use of the large-scale computing systems. Many application/architecture-dependent parameters that determine energy efficiency and resilience individually have causal effects with each other, which directly affect the trade-offs among performance, energy efficiency and resilience at scale. To enable high-efficiency management for large-scale High-Performance Computing (HPC) systems nowadays, quantitatively understanding the entangled effects among performance, energy efficiency, and resilience is thus required. While previous work focuses on exploring energysaving and resilience-enhancing opportunities separately, little has been done to theoretically and empirically investigate the interplay between energy efficiency and resilience at scale. In this article, by extending the Amdahl's Law and the Karp-Flatt Metric, taking resilience into consideration, we quantitatively model the integrated energy efficiency in terms of performance per Watt and showcase the trade-offs among typical HPC parameters, such as number of cores, frequency/voltage, and failure rates. Experimental results for a wide spectrum of HPC benchmarks on two HPC systems show that the proposed models are accurate in extrapolating resilience-aware performance and energy efficiency, and capable of capturing the interplay among various energy-saving and resilience factors. Moreover, the models can help find the optimal HPC configuration for the highest integrated energy efficiency, in the presence of failures and applied resilience techniques.

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EPROF: An energy/performance/reliability optimization framework for streaming applications

Cited by 14 publications

References 11 publications

Green Streams for data-intensive software

Green Streams for data-intensive software

A Study on Power Side Channels on Mobile Devices

Scalable Energy Efficiency with Resilience for High Performance Computing Systems

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