Issue No. 08 - Aug. (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.256
Francesco Paterna , Università di Bologna, Bologna
Andrea Acquaviva , Politecnico di Torino, Torino
Luca Benini , Università di Bologna, Bologna
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.
Resource management, Aging, Stress, Multicore processing, Transistors, Clocks, Time factors, multicore/single-chip multiprocessors, Resource management, Aging, Stress, Multicore processing, Transistors, Clocks, Time factors, scheduling and task partitioning, Reliability
F. Paterna, A. Acquaviva and L. Benini, "Aging-Aware Energy-Efficient Workload Allocation for Mobile Multimedia Platforms," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 1489-1499, 2013.