GPU Computing Gems

"...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) are designed to be parallel - having hundreds of cores versus traditional CPUs. Increasingly, you can [...]
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"...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) are designed to be parallel - having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for many computationally-intense applications - 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. Different application domains often pose similar algorithm problems, and researchers from diverse application domains often develop similar algorithmic strategies. "GPU Computing Gems" offers developers a window into diverse application areas, and the opportunity to gain insights from others' algorithm work that they may apply to their own projects. Learn from the leading researchers in parallel programming, who have gathered their solutions and experience in one volume under the guidance of expert area editors.Each chapter is written to be accessible to researchers from other domains, allowing knowledge to cross-pollinate across the GPU spectrum. "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, video and image processing. It covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more. It includes many examples that leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution. It offers insights and ideas as well as practical 'hands-on' skills you can immediately put to use.