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Section 1: Scientific Simulation
State of GPU Computing in Scientific Simulation
1: GPU-Accelerated Computation and Interactive Display of Molecular Orbitals
2: Large-Scale Chemical Informatics on GPUs
3: Dynamical Quadrature Grids: Applications in Density Functional Calculations
4: Fast Molecular Electrostatics Algorithms on GPUs
5: Quantum Chemistry: Propagation of Electronic Structure on GPU
6: An Efficient CUDA Implementation of the Tree-based Barnes Hut n-Body Algorithm
7: Leveraging the Untapped Computation Power of GPUs: Fast Spectral Synthesis Using Texture Interpolation
8: Black Hole Simulations with CUDA
9: Treecode and Fast Multipole Method for N-body Simulation with CUDA
10: Wavelet-based Density Functional Theory Calculation on Massively Parallel Hybrid Architectures
Section 2: Life Sciences
State of GPU Computing in Life Sciences
11: Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm
12: Massive Parallel Computing to Accelerate Genome-Matching
13: GPU-Supercomputer Acceleration of Pattern Matching
14: GPU Accelerated RNA Folding Algorithm
15: Temporal Data Mining for Neuroscience
Section 3: Statistical Modeling
State of GPU Computing in Statistical Modeling
16: Parallelization Techniques for Random Number Generations
17: Monte Carlo Photon Transport on the GPU
18: High Performance Iterated Function Systems
Section 4: Emerging Data-intensive Applications
State of GPU Computing in Data-intensive Applications
19: Large Scale Machine Learning
20: Multiclass Support Vector Machine
21: Template Driven Agent Based Modeling and Simulation with CUDA
22: GPU-Accelerated Ant Colony Optimization
Section 5: Electronic Design Automation
State of GPU Computing in Electronic Design Automation
23: High Performance Gate-Level Simulation with GP-GPUs
24: GPU-Based Parallel Computing for Fast Circuit Optimization
Section 6: Ray Tracing and Rendering
State of GPU Computing in Ray Tracing and Rendering
25: Lattice-Boltzmann Lighting Models
26: Path Regeneration for Random Walks
27: From Sparse Mocap to Highly-detailed Facial Animation
28: A Programmable Graphics Pipeline in CUDA for Order Independent Transparency
Section 7: Computer Vision
State of GPU Computing in Computer Vision
29: Fast Graph Cuts for Computer Vision
30: Visual Saliency Model on Multi-GPU
31: Real-Time Stereo on GPGPU Using Progressive Multi-Resolution Adaptive Windows
32: Real-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
33: Haar Classifiers for Object Detection with CUDA
Section 8: Video and Image Processing
State of GPU Computing in Video and Image Processing
34: Experiences on Image and Video Processing with CUDA and OpenCL
35: Connected Component Labeling in CUDA
36: Image Demosaicing
Section 9: Signal and Audio Processing
State of GPU Computing in Signal and Audio Processing
37: Efficient Automatic Speech Recognition on the GPU
38: Parallel LDPC Decoding
39: Large-Scale Fast Fourier Transform
Section 10: Medical Imaging
State of GPU Computing in Medical Imaging
40: GPU Acceleration of Iterative Digital Breast Tomosynthesis
41: Parallelization of Katsevich CT Image Reconstruction Algorithm on Generic Multi-Core Processors and GPGPU
42: 3-D Tomographic Image Reconstruction from Randomly Ordered Lines with CUDA
43: Using GPUs to Learn Effective Parameter Settings for GPU-Accelerated Iterative CT Reconstruction Algorithms
44: Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation
45: §¤1 Minimization in §¤1-SPIRiT Compressed Sensing MRI Reconstruction
46: Medical Image Processing Using GPU-accelerated ITK Image Filters
47: Deformable Volumetric Registration Using B-splines
48: Multi-scale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs
49: GPU-accelerated Brain Connectivity Reconstruction and Visualization in Large-Scale Electron Micrographs
50: Fast Simulation of Radiographic Images Using a Monte Carlo X-Ray Transport Algorithm Implemented in CUDA
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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.
¡á Features ¡á
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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