Guest Editors
Dr. Lordwin Cecil Prabhaker, Deemed to be University, India.
Dr. P. Suresh, Veltech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, India.
Dr. Jey Chelladurai, East Stroudsburg University, USA.
Summary
In recent years, there has been a clear shift towards High-Performance Computing (HPC) systems because it offers useful features such as faster computation, lower power consumption, and higher accuracy. HPC Infrastructure provides a wide range of engineering solutions to complex issues raised in Chemical Engineering, Computer-Aided Engineering, Communication Engineering, Electrical and Electronics Engineering, Mechanical Engineering, Geosciences, Meteorology, Biology, and other resource-intensive applications.
Present technologies such as analytics and AI require more powerful or accurate processes in computer, networking, and storage, depending on the range of their applications. Companies also benefit greatly when computing is very close to the origin of data anywhere. AI workloads such as in-depth learning and machine learning are built on top of the HPC infrastructure. As a result, more and more companies are increasingly using HPC solutions for AI-enabled innovation and productivity. These applications widely perform the following functions: 1. Data collection, 2. Data processing, 3. Data transfer, 4. Data storage and, 5. Data distribution. Also in recent days, most of the things connected over the internet (Internet of Things) were performing data analytics in HPC-based cloud environments.
The researchers, academics, hardware, and software designers are facing the need for innovative techniques to effectively manage such a complex system. The purpose of this special release is to collect the latest trends in high-performance computing systems and their related technologies such as multicore architecture, co-processing methods, optimization techniques. Papers on theoretical foundations and algorithms with strong analytical contributions are also encouraged for submission.
Keywords
• Parallel, Distributed, and Scalable modeling to improve the performance of HPC
• Theories and foundations of HPC
• Languages and compilers for HPC
• Parallel scheduling and load balancing techniques for HPC
• Energy-efficient computing infrastructure
• Analysis of Machine learning, deep learning, and artificial intelligence techniques in HPC
• High-Performance Computing for Big Data Processing
• Analysis and optimization of energy efficiency in Cloud, Fog and Edge Computing
• Evaluation and optimization of emerging energy-efficient computer and network architectures
• Approaches for designing and evaluating energy-efficient user applications
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