GPU Computing Gems Emerald Edition

Elsevier, 13 janv. 2011 - 886 pages
1 Commentaire

"...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

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Table des matières

Life Sciences
Statistical Modeling
Emerging DataIntensive Applications
Electronic Design Automation
Ray Tracing and Rendering
Computer Vision
Video and Image Processing
Signal and Audio Processing
Medical Imaging
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À propos de l'auteur (2011)

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 ( 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.

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