GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition Front Cover
16 Reviews
886 pages

Book 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 (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.

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 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, design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical , 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

Table of Contents

Section 1: Scientific Simulation
Chapter 1. GPU-Accelerated Computation and Interactive Display of Molecular Orbitals
Chapter 2. Large-Scale Chemical Informatics on GPUs
Chapter 3. Dynamical Quadrature Grids: Applications in Functional Calculations
Chapter 4. Fast Molecular Electrostatics Algorithms on GPUs
Chapter 5. Quantum Chemistry: Propagation of Electronic Structure on a GPU
Chapter 6. An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
Chapter 7. Leveraging the Untapped Computation Power of GPUs: Fast Spectral Synthesis Using Texture Interpolation
Chapter 8. Black Hole Simulations with CUDA
Chapter 9. Treecode and Fast Multipole Method for N-Body Simulation with CUDA
Chapter 10. Wavelet-Based Density Functional Theory Calculation on Massively Parallel Hybrid Architectures

Section 2: Life Sciences
Chapter 11. Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm
Chapter 12. Massive Parallel Computing to Accelerate Genome-Matching
Chapter 13. GPU-Supercomputer Acceleration of Pattern Matching
Chapter 14. GPU Accelerated RNA Folding Algorithm
Chapter 15. Temporal Data Mining for Neuroscience

Section 3: Statistical Modeling
Chapter 16. Parallelization Techniques for Random Number Generators
Chapter 17. Monte Carlo Photon Transport on the GPU
Chapter 18. High-Performance Iterated Function Systems

Section 4: Emerging Data-Intensive Applications
Chapter 19. Large-Scale Machine Learning
Chapter 20. Multiclass Support Vector Machine
Chapter 21. Template-Driven Agent-Based Modeling and Simulation with CUDA
Chapter 22. GPU-Accelerated Ant Colony Optimization

Section 5: Electronic Design Automation
Chapter 23. High-Performance Gate-Level Simulation with GP-GPUs
Chapter 24. GPU-Based Parallel Computing for Fast Circuit Optimization

Section 6: Ray Tracing and Rendering
Chapter 25. Lattice Boltzmann Lighting Models
Chapter 26. Path Regeneration for Random Walks
Chapter 27. From Sparse Mocap to Highly Detailed Facial Animation
Chapter 28. A Programmable Graphics Pipeline in CUDA for Order-Independent Transparency

Section 7: Computer Vision
Chapter 29. Fast Graph Cuts for Computer Vision
Chapter 30. Visual Saliency Model on Multi-GPU
Chapter 31. Real-Time Stereo on GPGPU Using Progressive Multiresolution Adaptive Windows
Chapter 32. Real-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
Chapter 33. Haar Classifiers for Object Detection with CUDA

Section 8: Video and Image Processing
Chapter 34. Experiences on Image and Video Processing with CUDA and OpenCL
Chapter 35. Connected Component Labeling in CUDA
Chapter 36. Image De-Mosaicing

Section 9: Signal and Audio Processing
Chapter 37. Efficient Automatic Speech Recognition on the GPU
Chapter 38. Parallel LDPC Decoding
Chapter 39. Large-Scale Fast Fourier Transform

Section 10: Medical Imaging
Chapter 40. GPU Acceleration of Iterative Digital Breast Tomosynthesis
Chapter 41. Parallelization of Katsevich CT Image Reconstruction Algorithm on Generic Multi-Core Processors and GPGPU
Chapter 42. 3-D Tomographic Image Reconstruction from Randomly Ordered Lines with CUDA
Chapter 43. Using GPUs to Learn Effective Parameter Settings for GPU-Accelerated Iterative CT Reconstruction Algorithms
Chapter 44. Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation
Chapter 45. ℓ1 Minimization in ℓ1-SPIRiT Compressed Sensing MRI Reconstruction
Chapter 46. Medical Image Processing Using GPU-Accelerated ITK Image Filters
Chapter 47. Deformable Volumetric Registration Using B-Splines
Chapter 48. Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs
Chapter 49. GPU-Accelerated Brain Connectivity Reconstruction and Visualization in Large-Scale Electron Micrographs
Chapter 50. Fast Simulation of Radiographic Images Using a Monte Carlo X-Ray Transport Algorithm Implemented in CUDA

Book Details

  • Title: GPU Computing Gems Emerald Edition
  • Length: 886 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2011-02-07
  • ISBN-10: 0123849888
  • ISBN-13: 9780123849885
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