cfaed Publications

Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Reference

Robert Khasanov, Jeronimo Castrillon, "Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping" , Proceedings of the 2020 Design, Automation and Test in Europe Conference (DATE), IEEE, pp. 909–914, Mar 2020. (Best paper award candidate E-Track, Video Presentation) [doi]

Abstract

Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the amount of available resources. This has considerably increased the complexity of analysis and optimization algorithms for runtime mapping of firm real-time applications. To reduce the runtime overhead, researchers have proposed to pre-compute partial mappings at compile time and have the runtime efficiently compute the final mapping. However, most existing solutions only compute a fixed mapping for a given set of running applications, and the mapping is defined for the entire duration of the workload execution. In this work we allow applications to adapt to the amount of available resources by using mapping segments. This way, applications may switch between different configurations with varied degree of parallelism. We present a runtime manager for firm real-time applications that generates such mapping segments based on partial solutions and aims at minimizing the overall energy consumption without deadline violations. The proposed algorithm outperforms the state-of-the-art approaches on the overall energy consumption by up to 13% while incurring an order of magnitude less scheduling overhead.

Bibtex

@InProceedings{khasanov_date20,
author = {Robert Khasanov and Jeronimo Castrillon},
title = {Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping},
booktitle = {Proceedings of the 2020 Design, Automation and Test in Europe Conference (DATE)},
year = {2020},
series = {DATE '20},
comment={Best paper award candidate E-Track, Video Presentation},
month = mar,
publisher = {IEEE},
location = {Grenoble, France},
isbn = {978-3-9819263-4-7},
pages = {909--914},
doi = {10.23919/DATE48585.2020.9116381},
url = {https://ieeexplore.ieee.org/document/9116381},
abstract = {Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the amount of available resources. This has considerably increased the complexity of analysis and optimization algorithms for runtime mapping of firm real-time applications. To reduce the runtime overhead, researchers have proposed to pre-compute partial mappings at compile time and have the runtime efficiently compute the final mapping. However, most existing solutions only compute a fixed mapping for a given set of running applications, and the mapping is defined for the entire duration of the workload execution. In this work we allow applications to adapt to the amount of available resources by using mapping segments. This way, applications may switch between different configurations with varied degree of parallelism. We present a runtime manager for firm real-time applications that generates such mapping segments based on partial solutions and aims at minimizing the overall energy consumption without deadline violations. The proposed algorithm outperforms the state-of-the-art approaches on the overall energy consumption by up to 13% while incurring an order of magnitude less scheduling overhead.},
}

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https://cfaed.tu-dresden.de/publications?pubId=2546


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