Aging-Aware Energy-Efficient Workload Allocation for Mobile Multimedia Platforms

Multicore platforms are characterized by increasing variability and aging effects that imply heterogeneity in core performance, energy consumption, and reliability. In particular, wear-out effects such as negative-bias-temperature-instability require runtime adaptation of system resource utilization...

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Published in:	IEEE transactions on parallel and distributed systems Vol. 24; no. 8; pp. 1489 - 1499
Main Authors:	Paterna, F., Acquaviva, A., Benini, L.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Aging Algorithms Allocations Chips Clocks Energy consumption Multicore processing multicore/single-chip multiprocessors Platforms Policies Reliability Resource management scheduling and task partitioning State of the art Strategy Stress Studies Time factors Transistors Workload
ISSN:	1045-9219, 1558-2183
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Abstract	Multicore platforms are characterized by increasing variability and aging effects that imply heterogeneity in core performance, energy consumption, and reliability. In particular, wear-out effects such as negative-bias-temperature-instability require runtime adaptation of system resource utilization to time-varying and uneven platform degradation, so as to prevent premature chip failure. In this context, task allocation techniques can be used to deal with heterogeneous cores and extend chip lifetime while minimizing energy and preserving quality of service. We propose a new formulation of the task allocation problem for variability affected platforms, which manages per-core utilization to achieve a target lifetime while minimizing energy consumption during the execution of rate-constrained multimedia applications. We devise an adaptive solution that can be applied online and approximates the result of an optimal, offline version. Our allocator has been implemented and tested on real-life functional workloads running on a timing accurate simulator of a next-generation industrial multicore platform. We extensively assess the effectiveness of the online strategy both against the optimal solution and also compared to alternative state-of-the-art policies. The proposed policy outperforms state-of-the-art strategies in terms of lifetime preservation, while saving up to 20 percent of energy consumption without impacting timing constraints.
AbstractList	Multicore platforms are characterized by increasing variability and aging effects that imply heterogeneity in core performance, energy consumption, and reliability. In particular, wear-out effects such as negative-bias-temperature-instability require runtime adaptation of system resource utilization to time-varying and uneven platform degradation, so as to prevent premature chip failure. In this context, task allocation techniques can be used to deal with heterogeneous cores and extend chip lifetime while minimizing energy and preserving quality of service. We propose a new formulation of the task allocation problem for variability affected platforms, which manages per-core utilization to achieve a target lifetime while minimizing energy consumption during the execution of rate-constrained multimedia applications. We devise an adaptive solution that can be applied online and approximates the result of an optimal, offline version. Our allocator has been implemented and tested on real-life functional workloads running on a timing accurate simulator of a next-generation industrial multicore platform. We extensively assess the effectiveness of the online strategy both against the optimal solution and also compared to alternative state-of-the-art policies. The proposed policy outperforms state-of-the-art strategies in terms of lifetime preservation, while saving up to 20 percent of energy consumption without impacting timing constraints.
Author	Benini, L. Paterna, F. Acquaviva, A.
Author_xml	– sequence: 1 givenname: F. surname: Paterna fullname: Paterna, F. email: francesco_paterna@brown.edu organization: Sch. of Eng., Brown Univ., Providence, RI, USA – sequence: 2 givenname: A. surname: Acquaviva fullname: Acquaviva, A. email: andrea.acquaviva@polito.it organization: DAUIN, Politec. di Torino, Turin, Italy – sequence: 3 givenname: L. surname: Benini fullname: Benini, L. email: luca.benini@unibo.it organization: Dept. of Electr. Eng. & Comput. Sci., Univ. di Bologna, Bologna, Italy
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SubjectTerms	Aging Algorithms Allocations Chips Clocks Energy consumption Multicore processing multicore/single-chip multiprocessors Platforms Policies Reliability Resource management scheduling and task partitioning State of the art Strategy Stress Studies Time factors Transistors Workload
Title	Aging-Aware Energy-Efficient Workload Allocation for Mobile Multimedia Platforms
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