Advanced Computing Techniques for Real-Time Data Processing and High-Performance Computing

Abstract

Real-time data processing and high-performance computing (HPC) have become essential components of modern applications across various industries, including healthcare, finance, telecommunications, and autonomous systems. The increasing volume, variety, and velocity of data generated by the Internet of Things (IoT), artificial intelligence (AI), and big data analytics have created new demands for computing systems that can process vast datasets in real time. Traditional computing architectures often struggle to meet these requirements, necessitating the adoption of advanced computing techniques for efficient real-time data processing and enhanced performance. This paper explores advanced computing techniques that are shaping the landscape of real-time data processing and HPC, including parallel processing, distributed computing, in-memory computing, and edge computing. Each of these methodologies offers unique benefits, such as improved scalability, reduced latency, and enhanced fault tolerance. Furthermore, emerging technologies such as AI and quantum computing present new opportunities for enhancing real-time processing capabilities. In addition to examining key techniques, this research addresses the challenges faced by real-time data processing systems, such as data management at scale, minimizing latency, ensuring fault tolerance, and optimizing energy efficiency. The integration of novel solutions, such as edge computing to minimize data transfer delays and in-memory computing for faster access to data, is also discussed. This paper provides a comprehensive overview of the advancements in computing architectures and strategies that enable real-time data processing and high-performance computing. Through detailed analysis, the research offers insights into current trends, the critical challenges faced, and future directions for optimizing real-time and HPC systems to meet the demands of increasingly complex applications. Three tables are presented to summarize techniques, their applications, and the challenges they address in the context of real-time computing and HPC.