An integrated taxonomy of standard indicators for ranking and selecting supercomputers

Abstract

AbstractDue to the ever‐increasing computing requirements of modern applications, supercomputers are at the centre of attraction as a platform for high‐performance computing. Although various features and indicators for testing and evaluating supercomputers are proposed in the literature, a comprehensive feature set to guide designers in comparing supercomputers and selecting an appropriate choice is not provided. Here, an integrated feature‐based taxonomy comprised of seven indicator groups including passive infrastructure, hardware, software, support and maintenance, service, business, and security features is proposed. Also, a case study using our proposed framework is provided and a comparison between some commercial and research supercomputers including Fugaku's ideal supercomputer, Sharif supercomputer, Aramco supercomputer, and ITU supercomputer is presented. Moreover, here, the authors’ proposed method is compared with the Top500 method, which shows that the authors’ proposed method facilitates the ranking, comparison, and selection of the appropriate supercomputer in various fields by considering various aspects of design and implementation. The ranking results show that Aramco supercomputer, ITU supercomputer, and Sharif supercomputer have 65.9%, 57.6%, and 48.2% of ideal supercomputer points, respectively.