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Introduces mechanical engineers to high-performance computing using the new generation of computers with vector and parallel processing capabilities that allow the solution to problems beyond the ken of traditional computers. The chapters present an introduction and overview, explain several methodo
The development of supercomputers has had considerable impact in computational mechanics. This book deals with the application of parallel processing with supercomputers and examines the problems of computational mechanics in a logical way.
Introduces mechanical engineers to high-performance computing using the new generation of computers with vector and parallel processing capabilities that allow the solution to problems beyond the ken of traditional computers. The chapters present an introduction and overview, explain several methodo
The recent rapid changes in computer technology has delivered to engineers a new technology to enhance the performance and capabilities of engineering computational software. Parallel processing promises to privide a quantum leap in the capabilities of engineering systems and make new appplications possible. However, many current computational mechanics techniques may have to be radically revised in order to take advantage of this new technology. Parallel computing also provides an opportunity to develop new more powerful techniques. The exciting aspect of parallel processing is that it is a technology which is available to all engineers and not restricted to a few. The transputer has provided the PC and the workstation with very high performance parallel systems. Minicomputers, mainframes and supercomputers offer multiprocessor systems. Completely new massivley parallel systems are also now available to engineers.
Included in this book are the keynote lectures presented at The First Euro-Conference on Parallel and Distributed Computing for Computational Mechanics, 26 April-1 May 1997, Lochinver, Scotland.
This book follows the previously published title, Solving Large-scale Problems in Mechanics, edited by M. Papadrakakis. This first volume to be published in the Wiley Series in Solving Large-scale Problems in Mechanics is devoted to high-performance computing using the new generation of computers with parallel and distributed computing capabilities. Parallel and distributed processing is a rapidly growing area of high technology where engineering applications lagged behind hardware advances. New algorithms and codes are required in order to exploit effectively modern computer architectures, as programs suitable for conventional computers achieve very modest performances on these new machines. There is therefore an urgent need to develop and test powerful solution and data handling techniques capable of exploiting the potential of modern computers and of accomplishing the solution of complex engineering problems in an acceptable computing time. This volume intends capturing the latest developments in the field and to serve as an essential reference book on the subject. It comprises a comprehensive state-of-the-art treatment of theory and practice, illustrated by extensive numerical examples.
This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kernels that arise in applications. The rest of the volume focuses on state-of-the-art applications from diverse domains. By structuring the volume around these two areas, it presents a comprehensive view of the application landscape for high-performance computing, while also enabling readers to develop new applications using the kernels. Readers will learn how to choose the most suitable parallel algorithms for any given application, ensuring that theory and practicality are clearly connected. Applications using these techniques are illustrated in detail, including: Computational materials science and engineering Computational cardiovascular analysis Multiscale analysis of wind turbines and turbomachinery Weather forecasting Machine learning techniques Parallel Algorithms in Computational Science and Engineering will be an ideal reference for applied mathematicians, engineers, computer scientists, and other researchers who utilize high-performance computing in their work.
Scientific computing has often been called the third approach to scientific discovery, emerging as a peer to experimentation and theory. Historically, the synergy between experimentation and theory has been well understood: experiments give insight into possible theories, theories inspire experiments, experiments reinforce or invalidate theories, and so on. As scientific computing has evolved to produce results that meet or exceed the quality of experimental and theoretical results, it has become indispensable.Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering. This edited volume serves as an up-to-date reference for researchers and application developers on the state of the art in scientific computing. It also serves as an excellent overview and introduction, especially for graduate and senior-level undergraduate students interested in computational modeling and simulation and related computer science and applied mathematics aspects.Contents List of Figures; List of Tables; Preface; Chapter 1: Frontiers of Scientific Computing: An Overview; Part I: Performance Modeling, Analysis and Optimization. Chapter 2: Performance Analysis: From Art to Science; Chapter 3: Approaches to Architecture-Aware Parallel Scientific Computation; Chapter 4: Achieving High Performance on the BlueGene/L Supercomputer; Chapter 5: Performance Evaluation and Modeling of Ultra-Scale Systems; Part II: Parallel Algorithms and Enabling Technologies. Chapter 6: Partitioning and Load Balancing; Chapter 7: Combinatorial Parallel and Scientific Computing; Chapter 8: Parallel Adaptive Mesh Refinement; Chapter 9: Parallel Sparse Solvers, Preconditioners, and Their Applications; Chapter 10: A Survey of Parallelization Techniques for Multigrid Solvers; Chapter 11: Fault Tolerance in Large-Scale Scientific Computing; Part III: Tools and Frameworks for Parallel Applications. Chapter 12: Parallel Tools and Environments: A Survey; Chapter 13: Parallel Linear Algebra Software; Chapter 14: High-Performance Component Software Systems; Chapter 15: Integrating Component-Based Scientific Computing Software; Part IV: Applications of Parallel Computing. Chapter 16: Parallel Algorithms for PDE-Constrained Optimization; Chapter 17: Massively Parallel Mixed-Integer Programming; Chapter 18: Parallel Methods and Software for Multicomponent Simulations; Chapter 19: Parallel Computational Biology; Chapter 20: Opportunities and Challenges for Parallel Computing in Science and Engineering; Index.