GPU Computing Gems: Emerald Edition - Rilegato

Rilegato

ISBN 10: 0123849888 ISBN 13: 9780123849885

Casa editrice: Morgan Kaufmann Pub, 2011

Riassunto

"...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010

Graphics processing units (GPUs) can do much more than render graphics. Scientists and researchers increasingly look to GPUs to improve the efficiency and performance of computationally-intensive experiments across a range of disciplines.

GPU Computing Gems: Emerald Edition brings their techniques to you, showcasing GPU-based solutions including:

• Black hole simulations with CUDA
• GPU-accelerated computation and interactive display of molecular orbitals
• Temporal data mining for neuroscience
• GPU -based parallelization for fast circuit optimization
• Fast graph cuts for computer vision
• Real-time stereo on GPGPU using progressive multi-resolution adaptive windows
• GPU image demosaicing
• Tomographic image reconstruction from unordered lines with CUDA
• Medical image processing using GPU -accelerated ITK image filters
• 41 more chapters of innovative GPU computing ideas, written to be accessible to researchers from any domain

GPU Computing Gems: Emerald Edition is the first volume in Morgan Kaufmann's Applications of GPU Computing Series, offering the latest insights and research in computer vision, electronic design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, and video / image processing.

• Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more
• Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution
• Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use

Su questo libro

Informazioni sull?autore

Wen-mei W. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests are in the area of architecture, implementation, compilation, and algorithms for parallel computing. He is the chief scientist of Parallel Computing Institute and director of the IMPACT research group (www.impact.crhc.illinois.edu). He is a co-founder and CTO of MulticoreWare. For his contributions in research and teaching, he received the ACM SigArch Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the Tau Beta Pi Daniel C. Drucker Eminent Faculty Award, the ISCA Influential Paper Award, the IEEE Computer Society B. R. Rau Award and the Distinguished Alumni Award in Computer Science of the University of California, Berkeley. He is a fellow of IEEE and ACM. He directs the UIUC CUDA Center of Excellence and serves as one of the principal investigators of the NSF Blue Waters Petascale computer project. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.

Dalla quarta di copertina

Practical techniques straight from the leading minds in general purpose GPU research.

Graphics Processing Units (GPUs) are designed to be parallel having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for any computationally-intense operation not just for graphics. If you're facing the challenge of programming systems to effectively use these massively parallel processors to achieve efficiency and performance goals, GPU Computing Gems provides a wealth of tested, proven GPU techniques. Learn from the leading researchers in concurrent programming, who have gathered their insights and experience in one volume under the guidance of NVIDIA and GPU expert Wen-mei Hwu.

Features

• Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more
• Many examples utilize NVIDIA Compute Unified Device Architecture (CUDA), the most widely-adopted GPU programming tool
• Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use

About the Editor-in-Chief

Wen-Mei Hwu Co-author of Programming Massively Parallel Processors and Jerry Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign

Dettagli dell?edizione

• Editore: Morgan Kaufmann Pub
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0123849888
• ISBN 13: 9780123849885
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 865
• Redattore: Hwu Wen-mei W.
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile