Bard: A unified framework for managing soft timing and power constraints

Imes, Connor; Hoffmann, Henry

doi:10.1109/samos.2016.7818328

Cited by 14 publications

(10 citation statements)

References 37 publications

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“…We used the on board energy sensors to measure the entire system. These sensors have been checked against external power measurement instruments and proven to be accurate [17]. To measure the energy consumption, we have developed a lightweight runtime to take readings from the on-board energy sensors at a frequency of 10 samples per second.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

2017

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Heterogeneous multi-core architectures consisting of CPUs and GPUs are commonplace in today's embedded systems. These architectures offer potential for energy efficient computing if the application task is mapped to the right core. Realizing such potential is challenging due to the complex and evolving nature of hardware and applications. This paper presents an automatic approach to map OPENCL kernels onto heterogeneous multi-cores for a given optimization criterion -whether it is faster runtime, lower energy consumption or a trade-off between them. This is achieved by developing a machine learning based approach to predict which processor to use to run the OPENCL kernel and the host program, and at what frequency the processor should operate. Instead of hand-tuning a model for each optimization metric, we use machine learning to develop a unified framework that first automatically learns the optimization heuristic for each metric off-line, then uses the learned knowledge to schedule OPENCL kernels at runtime based on code and runtime information of the program. We apply our approach to a set of representative OPENCL benchmarks and evaluate it on an ARM big.LITTLE mobile platform. Our approach achieves over 93% of the performance delivered by a perfect predictor. We obtain, on average, 1.2x, 1.6x, and 1.8x improvement respectively for runtime, energy consumption and the energy delay product when compared to a comparative heterogeneous-aware OPENCL task mapping scheme.

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Section: Evaluation Methodologymentioning

confidence: 99%

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

2017

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“…We use the on board energy sensors to measure the energy consumption of the entire system. These sensors have been proven to be accurate [10]. We implemented our approach in Chromium (v64.0) which is compiled using GCC (v7.2).…”

Section: B Predictive Modelingmentioning

confidence: 99%

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Ren¹,

Wang²,

Fang³

et al. 2024

IEEE Comput. Arch. Lett.

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Web browsing is an important application domain, but it imposes a significant power burden on mobile devices. While heterogeneous multi-cores offer the potential for energy-efficient computing, existing web browsers fail to exploit the hardware to optimize mobile web browsing. Our work aims to offer a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict the optimal processor setting for rendering the web content. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the Chromium browser and testing it on a representative big.LITTLE mobile platform. We apply our approach to the top 1,000 hottest websites across seven typical networking environments. Our approach achieves over 80% of the performance delivered by a perfect predictor. Our approach achieves over 30%, 50%, and 60% improvement respectively for load time, energy consumption and the energy delay product when compared to two state-of-the-art approaches.

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“…Most of such scheduling techniques are based on predicting the execution latency of the next schedulable event using history information [21,70], machine learning [56,58,72], and a combination of profiling and machine learning [44]. Others have studied applying control theory to ACMP scheduling [32,39,40].…”

Section: Related Workmentioning

confidence: 99%

Pes

Zhu

2019

Proceedings of the 46th International Symposium on Computer Architecture

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Web applications are gradually shifting toward resource-constrained mobile devices. As a result, the Web runtime system must simultaneously address two challenges: responsiveness and energy-efficiency. Conventional Web runtime systems fall short due to their reactive nature: they react to a user event only after it is triggered. The reactive strategy leads to local optimizations that schedule event executions one at a time, missing global optimization opportunities.This paper proposes Proactive Event Scheduling (PES). The key idea of PES is to proactively anticipate future events and thereby globally coordinate scheduling decisions across events. Specifically, PES predicts events that are likely to happen in the near future using a combination of statistical inference and application code analysis. PES then speculatively executes future events ahead of time in a way that satisfies the QoS constraints of all the events while minimizing the global energy consumption. Fundamentally, PES unlocks more optimization opportunities by enlarging the scheduling window, which enables coordination across both outstanding events and predicted events. Hardware measurements show that PES reduces the QoS violation and energy consumption by 61.2% and 26.5%, respectively, over the Android's default Interactive CPU governor. It also reduces the QoS violation and energy consumption by 63.1% and 17.9%, respectively, compared to EBS, a state-of-the-art reactive scheduler.

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Bard: A unified framework for managing soft timing and power constraints

Cited by 14 publications

References 37 publications

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

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