Publisher
Springer International Publishing
Reference24 articles.
1. Ahmed, K., Tasnim, S., Yoshii, K.: Energy-efficient heterogeneous computing of parallel applications via power capping. In: 2020 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1237–1242, IEEE, December 2020
2. Cai, E., Juan, D.-C., Stamoulis, D., Marculescu, D.: Learning-based Power and Runtime Modeling for Convolutional Neural Networks (2019). https://personal.utdallas.edu/~fxc190007/courses/20S-7301/example-report-2.pdf. Accessed 8 June 2022
3. Ciesielczyk, T., et al.: An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping. J. Sched. 24(5), 489–505 (2021)
4. Czarnul, P., Proficz, J., Krzywaniak, A.: Energy-aware high-performance computing: survey of state-of-the-art tools, techniques, and environments. Sci. Program. 2019, 8348791:1–8348791:19 (2019)
5. García-Martín, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75–88 (2019)
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