CUDA by Example: An Introduction to General-Purpose GPU Programming

Sanders / Kandrot, Jason

ISBN 10: 0131387685 / ISBN 13: 9780131387683

Verlag: Addison-Wesley Professional, 2010

Beschreibung

a clean, bright and tight copy of 290pp. no blemishes or markings. a near fine condition copy. Bestandsnummer des Verkäufers 2255

Bibliografische Details

Titel: CUDA by Example: An Introduction to ...

Verlag: Addison-Wesley Professional

Erscheinungsdatum: 2010

Einband: Soft cover

Zustand: Near Fine

Auflage: 1st Edition

Über diesen Titel

Inhaltsangabe:

Cuda By Example: An Introduction To General-Purpose Gpu Programming by Jason Sanders & Edward Kandrot, 9780131387683, Pearson

Reseña del editor:

"This book is required reading for anyone working with accelerator-based computing systems." -From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required-just the ability to program in a modestly extended version of C. CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You'll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. Major topics covered include Parallel programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams CUDA C on multiple GPUs Advanced atomics Additional CUDA resources All the CUDA software tools you'll need are freely available for download from NVIDIA. http://developer.nvidia.com/object/cuda-by-example.html