Bøger i Chapman & Hall/CRC Computational Science serien

Aimed at beginning students in computer science, mathematics, statistics, and engineering, Introduction to Elementary Computational Modeling: Essential Concepts, Principles, and Problem Solving focuses on fundamentals, helping the next generation of scientists and engineers hone their problem solving skills.

This book focuses on the ecosystems surrounding the world's leading centers for high performance computing (HPC). This third volume will be a continuation of the two previous volumes, and will include other HPC ecosystems using the same chapter outline: description of a flagship system, major application workloads, facilities, and sponsors.

Emphasizing analytical skill development and problem solving, this book shows how to implement computational models using the flexible and easy-to-use Python programming language. It provides the foundation for more advanced work in scientific computing. The book uses the Python programming language interpreter and several packages from the huge

This text provides all the material for a graduate or senior undergraduate course that focuses on the architecture of multicore processors. The book is also useful as a reference for professionals who deal with programming on multicore or designing multicore chips. It addresses programming issues in shared memory multiprocessors, covers the arch

This book provides computational scientists and engineers with a reference book containing information about the best software engineering practices to employ in the development of computational software. The book contains case studies and real world examples of the use of these practices, through contributions from key people in the field.

The book introduces the principles of mathematical modeling in science, engineering, and social science as well as basic skills of computer programming. The book is aimed at majors in STEM disciplines that need to understand how to create, analyze, and test mathematical models.

This book addresses the significant challenge in designing applications to effectively use high-performance computing architectures and offers methods to more effectively restructure applications to take advantage of these powerful nodes. The authors discuss the benefit of vectorization, providing examples run on current systems.

This book focuses on the development of scalable and performance portable scientific applications for future exascale computer systems. It centers on programming practices of application developers to achieve scalability on high-end computer systems while maintain architectural and performance portability for different computer technologies

This book describes the coarray parallel programming model that will be part of the next standard version of the Fortran language. It provides a practical guide for Fortran programmers who want to start writing parallel applications using coarrays as soon as the compilers become commercially available.

Suitable for senior undergraduate and first-year graduate students, this book covers the varied and interconnected aspects of Grid computing, including how to design a system infrastructure and Grid portal; job submission and scheduling; Grid security; Grid computing services and software tools; workflow editors; and, Grid-enabling applications.

This book is a well-recognized and leading guidebook on High Performance Computing for a broad audience of readers from industry and academia. The new edition is completely reorganized and updated to include the latest research in HPC. A new introductory chapter and a new chapter on accelerated computing have been added to the book to keep pace with recent advances.

Suitable for scientists, engineers, and students, this book presents a practical introduction to high performance computing (HPC). It discusses the architecture of modern processors, providing a solid understanding of the performance potentials and limitations of current architectures and code.

This book focuses on the ecosystems surrounding the world¿s leading centers for high performance computing (HPC). This third volume will be a continuation of the two previous volumes, and will include other HPC ecosystems using the same chapter outline: description of a flagship system, major application workloads, facilities, and sponsors.

Emphasizing analytical skill development and problem solving, this book shows how to implement computational models using the flexible and easy-to-use Python programming language. It provides the foundation for more advanced work in scientific computing. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author¿s website.

Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models. The first five chapters present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance. The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren''t discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then surveys parallel algorithms. ?  

This book focuses on the development of scalable and performance portable scientific applications for future exascale computer systems. It centers on programming practices of application developers to achieve scalability on high-end computer systems while maintain architectural and performance portability for different computer technologies.

Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material. The text introduces methods for solving various scheduling problems, including resource-constrained project scheduling, machine scheduling, and job scheduling. It covers both the foundations in scheduling and modern developments, such as online scheduling. Along with a number of examples, theorems, and pedagogical proofs, the book provides in-depth coverage of key application fields.

The book introduces the principles of mathematical modeling in science, engineering, and social science as well as basic skills of computer programming. The book is aimed at majors in STEM disciplines that need to understand how to create, analyze, and test mathematical models.

This book teaches GPU programming by introducing CPU multi-threaded programming and bases GPU massively-parallel programming on this foundation. The differences among families of GPUs are also studied. The book also explores CUDA libraries, OpenCL, GPU programming with other languages and API libraries, and the deep learning library cuDNN.

This book addresses the significant challenge in designing applications to effectively use high-performance computing architectures and offers methods to more effectively restructure applications to take advantage of these powerful nodes. The authors discuss the benefit of vectorization, providing examples run on current systems.

This book provides computational scientists and engineers with a reference book containing information about the best software engineering practices to employ in the development of computational software. The book contains case studies and real world examples of the use of these practices, through contributions from key people in the field.