Latest Trends in Parallel and Distributed Computing

Abstract

The exponential growth of digital data and computational requirements has significantly increased the demand for advanced computing paradigms capable of processing large-scale workloads efficiently. Parallel and distributed computing have emerged as key technologies that enable high-performance processing by utilizing multiple processors and interconnected computing nodes. These paradigms play an essential role in modern applications such as artificial intelligence, big data analytics, cloud computing, and scientific simulations. This paper provides a comprehensive overview of recent developments in parallel and distributed computing, focusing on heterogeneous computing architectures, distributed machine learning frameworks, cloud-native infrastructures, and energy-efficient computing techniques. Additionally, major technical challenges including communication overhead, resource management, and system scalability are discussed. Finally, potential future research directions are explored, including edge computing integration, intelligent scheduling mechanisms, and hybrid computing environments. The study highlights how emerging technologies continue to reshape large-scale computing systems and improve computational efficiency across various domains.