GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition

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q...the perfect companion to Programming Massively Parallel Processors by Hwu a Kirk.q -Nicolas Pinto, Research Scientist at Harvard a 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 qhands-onq skills you can immediately put to useThis becomes the critical bottleneck: with moleculemajor multiset layout, the CUDA Visual Profiler indicates that the kernela#39;s arithmetic throughput is only 15% of peak on a GeForce GTS 250. Transposing the multiset layout, such that allanbsp;...

Title:GPU Computing Gems Emerald Edition
Publisher:Elsevier - 2011-01-13


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