Advanced Computational Techniques for Large-Scale Data Manipulation in High-Performance Computing

Abstract

Advanced computational techniques are crucial for effectively manipulating large-scale data within high-performance computing (HPC) environments. HPC utilizes parallel data processing to enhance computing performance and handle complex calculations, which is essential for cutting-edge technologies like AI, machine learning, and IoT that require processing vast amounts of data. This review examines recent advancements in computational methods, focusing on modeling, simulation, and optimization of complex systems, and emphasizes the integration of Artificial Intelligence (AI) with traditional computational approaches. Modern algorithms, including AI methods, offer novel strategies for materials production and optimization within energy systems, demonstrating significant improvements in accuracy and efficiency. The development of new machine learning models and algorithms addresses the challenges of efficient training, accelerated inference, and improved generalization. Techniques such as LSTM-Jump, which skips unimportant information in text, and QANet, a recurrence-free model for parallel training and inference, exemplify these advancements. Furthermore, algorithms like DSPDC and DSCOVR enable fast training of neural networks and distributed optimization on parameter servers, achieving linear convergence rates. High-performance computing systems can perform quadrillions of calculations per second, vastly outperforming regular computers and workstations. These advancements not only enhance computational speed but also overcome computational barriers faced by conventional systems