Parallel and Distributed Algorithms for Efficient Large-Scale Computations

Abstract

The ever-growing demand for computational power in diverse domains such as scientific research, big data analytics, machine learning, and simulations has necessitated the development of parallel and distributed algorithms. These algorithms aim to enhance computational efficiency by leveraging multiple processors or machines to handle large datasets and complex tasks. Parallel algorithms execute multiple tasks concurrently on multiple processors, while distributed algorithms coordinate multiple machines to work on different portions of a problem. This review explores the concepts, techniques, and applications of parallel and distributed algorithms in large-scale computations. We examine their theoretical foundations, their real-world applications, and the challenges associated with implementing and optimizing such algorithms.