TIM-8122 V1

Assran, B. M., Aytekin, A., Feyzmahdavian, H. R., Johansson, M., & Rabbat, M. G. (2020). Advances in asynchronous parallel and distributed optimization. Proceedings of the IEEE 108(11), 2013–2031. https://doi.org/10.1109/JPROC.2020.3026619
The author discusses several advancements in machine learning and asynchronous parallel and distributed optimization methods in the past decade.

Wei, X., Sun, Q., Liu, X., Yue, C., Liang, S. Y., & Wang, L. (2022). Research on parallel distributed clustering algorithm applied to cutting parameter optimization. The International Journal of Advanced Manufacturing Technology, 120(11–12), 7895–7904. https://doi.org/10.1007/s00170-022-09252-7
The author proposes an improved K-means clustering algorithm based on the concept of distributed clustering in a cloud computing environment. The improved algorithm (T.K-means) is combined with the MapReduce computing framework of the Hadoop platform to realize parallel computing so as to perform processing tasks of massive data.

Rotman, D. (n.d.). We’re not prepared for the end of Moore’s Law. MIT Technology Review.
This is an MIT Technology Review blog discussing the progress of chip manufacturing and Moore’s Law.

Parizad, A., & Hatziadoniu, C. (2022). Deep learning algorithms and parallel distributed computing techniques for high-resolution load forecasting applying hyperparameter optimization. IEEE Systems Journal 16(3), 3758–3769. https://doi.org/10.1109/JSYST.2021.3130080
The author aims to build an accurate long-term load forecasting model based on deep learning (DL) algorithms.

Ziye Liu, Fanghong Guo, Wei Wang, & Xiaoqun Wu. (2023). A distributed parallel optimization algorithm via alternating direction method of multipliers. IET Control Theory & Applications, 17(7), 896–905. https://doi.org/10.1049/cth2.12421
The author discusses a new distributed parallel ADMM algorithm, which allows the agents to update their local states and dual variables in a completely distributed and parallel manner.