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GPU Computing Gems
역/저자 원메이 W. 후
ISBN 9780123849885
판형 248*197
페이지 865
정가 49,000 원
 
Private cloud computing networks offer real solutions for corporate network engineers and designers, consolidating diverse enterprise systems into one that is cloud-based and can be accessed by your end-users seamlessly, regardless of their location or changes in overall demand. Expert authors Steve Smoot and Nan Tam distill their years of networking experience to describe how to build enterprise networks over the cloud. With their techniques you’ll cut the costs of adding new hardware and increase the flexibility of your enterprise, while maintaining the security and control of an internal network. You will learn how network optimization, virtualization, and next-generation data centers work together to allow IT resources to be scaled up and down to provide on-demand services. 

■ Features ■ 
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