Advances in Asynchronous Parallel and Distributed Optimization

• Published in: Proceedings of the IEEE 2020-11, Vol.108 (11), p.2013-2031
• Main Authors: Assran, By Mahmoud, Aytekin, Arda, Feyzmahdavian, Hamid Reza, Johansson, Mikael, Rabbat, Michael G.
• Format: Article
• Language: English
• Subjects: Computational efficiency; Computational modeling; Convergence; Design optimization; Distributed algorithms; Machine learning; machine learning algorithms; Microprocessors; Optimization; Optimization methods; parallel algorithms; Processors; Program processors
• ISSN: 0018-9219; 1558-2256; 1558-2256
• DOI: 10.1109/JPROC.2020.3026619

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.