GPU Computing Gems Emerald Edition, 1st Edition

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