Fujian Province University Quanzhou

Fujian, China

Fujian Province University Quanzhou

Fujian, China

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Wu X.,Quanzhou Normal University | Wu X.,Fujian Provincial Key Laboratory of Data Intensive Computing | Wu X.,Fujian Province University Quanzhou | Zeng Y.,Quanzhou Normal University | And 3 more authors.
Proceedings - 2013 International Conference on Information Science and Cloud Computing Companion, ISCC-C 2013 | Year: 2014

Chip Multiprocessor (CMP) has become computing engine for a wide spectrum of applications due to its higher throughput and better energy efficiency. The problem of optimal task-to-core allocation with the minimum energy consumption has been proven to be NP-hard. In order to solve the energy-efficient real-time task mapping in the voltage frequency islands (VFI) based multicore system, we propose a heuristics EEGA (Energy-Efficient and Genetic Algorithm) to address the problem. During the iteration process of the algorithm, the energy consumption of the processor can be gradually optimized by the selection, crossover and mutation operators. Experimental results show that when compared with other energy-efficient mapping algorithms, our proposed approach can gain better performance with regard to the energy efficiency and schedulability ratio. © 2013 IEEE.

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