High Performance Computing Applications In Numerical Simulation And Edge Computing

This book constitutes the referred proceedings of two workshops held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018, in Beijing, China, in June 2018. This volume presents the papers that have been accepted for the following workshops: Second International Workshop on High Performance Computing for Advanced Modeling and Simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and First International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018. The 20 full papers presented during HPCMS 2018 and HiDEC 2018 were carefully reviewed and selected from numerous submissions. The papers reflect such topics as computing methodologies; parallel algorithms; simulation types and techniques; machine learning.

High Performance Computing Systems and Applications contains a selection of fully refereed papers presented at the 14th International Conference on High Performance Computing Systems and Applications held in Victoria, Canada, in June 2000. This book presents the latest research in HPC Systems and Applications, including distributed systems and architecture, numerical methods and simulation, network algorithms and protocols, computer architecture, distributed memory, and parallel algorithms. It also covers such topics as applications in astrophysics and space physics, cluster computing, numerical simulations for fluid dynamics, electromagnetics and crystal growth, networks and the Grid, and biology and Monte Carlo techniques. High Performance Computing Systems and Applications is suitable as a secondary text for graduate level courses, and as a reference for researchers and practitioners in industry.

This book covers the results obtained in the Tera op Workbench project during a four years period from 2004 to 2008. The Tera op Workbench project is a colla- ration betweenthe High PerformanceComputingCenter Stuttgart (HLRS) and NEC Deutschland GmbH (NEC-HPCE) to support users to achieve their research goals using high performance computing. The Tera op Workbench supports users of the HLRS systems to enable and - cilitate leading edge scienti c research. This is achieved by optimizing their codes and improving the process work ow which results from the integration of diff- ent modules into a “hybrid vector system”. The assessment and demonstration of industrial relevance is another goal of the cooperation. The Tera op Workbench project consists of numerous individual codes, grouped together by application area and developed and maintained by researchers or c- mercial organizations. Within the project, several of the codes have shown the ab- ity to reach beyond the TFlop/s threshold of sustained performance. This created the possibility for new science and a deeper understanding of the underlying physics. The papers in this book demonstrate the value of the project for different scienti c areas.

The book presents the state of the art in high performance computing and simulation on modern supercomputer architectures. It covers trends in hardware and software development in general and specifically the future of vector-based systems and heterogeneous architectures. The application contributions include computational fluid dynamics, physics, chemistry, astrophysics, and biology. Innovative application fields like multiphysics simulations and material science are presented.

This book constitutes selected revised and extended papers from the 10th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2020, Barnaul, Russia, in May 2020. Due to the COVID-19 pancemic the conference was partly held in virtual mode. The 14 full papers presented in this volume were thoroughly reviewed and selected form 51 submissions. The papers are organized in topical sections on hardware for high-performance computing and its applications; information technologies and computer simulation of physical phenomena.

High-performance computing (HPC) describes the use of connected computing units to perform complex tasks. It relies on parallelization techniques and algorithms to synchronize these disparate units in order to perform faster than a single processor could, alone. Used in industries from medicine and research to military and higher education, this method of computing allows for users to complete complex data-intensive tasks. This field has undergone many changes over the past decade, and will continue to grow in popularity in the coming years. Innovative Research Applications in Next-Generation High Performance Computing aims to address the future challenges, advances, and applications of HPC and related technologies. As the need for such processors increases, so does the importance of developing new ways to optimize the performance of these supercomputers. This timely publication provides comprehensive information for researchers, students in ICT, program developers, military and government organizations, and business professionals.

INTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment. The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters demonstrate stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. Further chapters address the challenges encountered by industries in integrating these digital technologies into their operational activities, as well as the opportunities for this integration. In addition, the reader will find: Systemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems; Highlights of the current and highly relevant topics in manufacturing management; Structured presentations resolving the issues being faced by many real-world applications in a broad range of areas such as smart supply chains, knowledge management, intelligent inventory management, IoT adoption in manufacturing management, and more; Intelligent techniques for sustainable practices in industrial waste management. Audience The book will be used by researchers, industry engineers, and data scientists/AI specialists working in industrial engineering, mechanical engineering, production engineering, manufacturing engineering, and operations and supply chain management. The book will also be valuable to the service sector industry, such as logistics and those implementing smart cities.

Industrial Applications of High-Performance Computing: Best Global Practices offers a global overview of high-performance computing (HPC) for industrial applications, along with a discussion of software challenges, business models, access models (e.g., cloud computing), public-private partnerships, simulation and modeling, visualization, big data analysis, and governmental and industrial influence. Featuring the contributions of leading experts from 11 different countries, this authoritative book: Provides a brief history of the development of the supercomputer Describes the supercomputing environments of various government entities in terms of policy and service models Includes a case study section that addresses more subtle and technical aspects of industrial supercomputing Shows how access to supercomputing matters, and how supercomputing can be used to solve large-scale and complex science and engineering problems Emphasizes the need for collaboration between companies, political organizations, government agencies, and entire nations Industrial Applications of High-Performance Computing: Best Global Practices supplies computer engineers and researchers with a state-of-the-art supercomputing reference. This book also keeps policymakers and industrial decision-makers informed about the economic impact of these powerful technological investments.