Key Features
- 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
Description
"...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.
Readership
computer programmers, software engineers, hardware engineers, computer science students
GPU Computing Gems Emerald Edition, 1st Edition
Editor’s Introduction: State of GPU Computing
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: l1 Minimization in l1-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
