ISI Report

Algebraic multigrid (AMG) is a powerful technique for solving large-scale linear systems arising in various scientific and engineering applications. However, the scalability of AMG can be limited by several factors, such as the size and complexity of the system, the selection of AMG parameters, and the available computational resources. Cloud computing has emerged as a promising technology for addressing these challenges, by providing access to scalable and flexible computing resources. In this article, we explore the potential of cloud computing for improving the scalability of AMG-based solvers, by reviewing the existing literature, discussing the challenges and opportunities, and proposing a research methodology for future studies. Algebraic multigrid (AMG) is a powerful tool for solving large-scale linear systems arising from various scientific and engineering applications. However, the scalability of AMG can be limited when dealing with very large systems, requiring sophisticated algorithms and computing resources. Cloud computing offers a promising solution to this issue, providing scalable, on-demand, and cost-effective access to computing resources. In this paper, we investigate the potential of cloud computing to enhance the scalability and performance of AMG. We propose a hybrid cloud approach that combines the advantages of both private and public clouds, and present a comprehensive evaluation of this approach on a set of large-scale benchmark problems. Our results show that our hybrid cloud approach can significantly improve the scalability and performance of AMG, making it an effective tool for solving large-scale problems in a cost-effective manner.