Books in the Chapman & Hall/CRC Computational Science series

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.

Teaches students how to model and explore the dynamics of systems. This book shows how to use the powerful tool of Simulink to investigate and form intuitions about the behavior of dynamical systems. It covers ordinary differential equations, numerical integration algorithms, and time-step simulation.

A work on the use of Cell BE and GPUs as accelerators for numerical kernels, algorithms, and computational science and engineering applications. It covers a range of topics on the increased role of these accelerators in scientific computing.

With a number of applications, peer-to-peer (P2P) computing is a form of distributed processing that continues to increase in use. This book provides a technical survey of existing P2P applications, various enabling technologies and protocols, and evolving research issues, such as network topology control and incentive providing mechanisms.

Focusing on recent, major ecosystems in HPC, this book brings together the vast interconnected aspects behind state-of-the-art HPC. It explains all the factors involved in making the world's leading HPC centers successful, from architectures, applications, facilities, and software to scientists, administrators, and sponsors. The first part of the book examines significant trends in applications, performance, software, and hardware. The second part provides a detailed look at the ecosystem, science, and organization of particular HPC sites. The last part of the book addresses the roles of clouds and grids in HPC.

Collecting scattered knowledge into one coherent account, this book provides a compendium of both classical and recently developed results on reversible computing. It offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach. It explores up-and-coming theories, techniques, and tools for the application of reversible computing. The topics covered span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing.

Written by one of the foremost experts in high-performance computing and the inventor of Gustafson¿s Law, this groundbreaking book explains a new approach to computer arithmetic: the universal number (unum). The unum encompasses all IEEE floating-point formats as well as fixed-point and exact integer arithmetic. This new number type obtains more accurate answers than floating-point arithmetic yet uses fewer bits in many cases, saving memory, bandwidth, energy, and power. Richly illustrated in color, the book is accessible to anyone who uses computers for technical calculations.

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.