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- W2524173768 abstract "The need for energy-aware computing has become increasingly demanding. However, high-performance computing is always users' pursuit. The key is how to maintain high performance meanwhile consuming an acceptable amount of energy. One feasible method is the energy-aware automatic tuning. In this paper, we implemented and compared several Differential Evolution (DE) based auto-tuning algorithms on an innovative many-core platform, in an effort to improve the energy consumption and energy delay product (EDP) of the entire platform. Our results show that Adaptive Differential Evolution algorithm is able to achieve better energy consumption as well as EDP than other DE algorithms tested. It also converges faster. We believe DE algorithm is an effective method that can be applied towards energy-efficient computing on manycore platforms." @default.
- W2524173768 created "2016-10-07" @default.
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- W2524173768 date "2016-08-01" @default.
- W2524173768 modified "2023-09-26" @default.
- W2524173768 title "Energy-Aware Automatic Tuning on Many-Core Platform via Differential Evolution" @default.
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- W2524173768 doi "https://doi.org/10.1109/icppw.2016.46" @default.
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