GPU Computing Gems Emerald Edition

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• Publisher: Morgan Kaufmann
• Year: 2011
• Print ISBN: 9780123849885
• eBook ISBN: 9780123849892
• Topic: Computer Science
• Subtopic: Hardware
• Index: Computer Science

GPU Computing Gems Emerald Edition offers practical techniques in parallel computing using graphics processing units (GPUs) to enhance scientific research. The first volume in Morgan Kaufmann's Applications of GPU Computing Series, this book offers the latest insights and research in computer vision, electronic design automation, and emerging data-intensive applications. It also covers life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, video and image processing. This book is intended to help those who are facing the challenge of programming systems to effectively use GPUs to achieve efficiency and performance goals. It offers developers a window into diverse application areas, and the opportunity to gain insights from others' algorithm work that they may apply to their own projects. Readers will learn from the leading researchers in parallel programming, who have gathered their solutions and experience in one volume under the guidance of expert area editors. Each chapter is written to be accessible to researchers from other domains, allowing knowledge to cross-pollinate across the GPU spectrum. Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution. The insights and ideas as well as practical hands-on skills in the book can be immediately put to use. Computer programmers, software engineers, hardware engineers, and computer science students will find this volume a helpful resource. For useful source codes discussed throughout the book, the editors invite readers to the following website: …" - 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

• Cover Image
• Table of Contents
• Front Matter
• Copyright
• Editors, Reviewers, and Authors
• Introduction
• Introduction
• Chapter 1. GPU-Accelerated Computation and Interactive Display of Molecular Orbitals
• Chapter 2. Large-Scale Chemical Informatics on GPUs
• Chapter 3. Dynamical Quadrature Grids
• Chapter 4. Fast Molecular Electrostatics Algorithms on GPUs
• Chapter 5. Quantum Chemistry
• Chapter 6. An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
• Chapter 7. Leveraging the Untapped Computation Power of GPUs
• 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
• Introduction
• 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
• Introduction
• Chapter 16. Parallelization Techniques for Random Number Generators
• Chapter 17. Monte Carlo Photon Transport on the GPU
• Chapter 18. High-Performance Iterated Function Systems
• Introduction
• 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
• Introduction
• Chapter 23. High-Performance Gate-Level Simulation with GP-GPUs
• Chapter 24. GPU-Based Parallel Computing for Fast Circuit Optimization
• Introduction
• 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
• Introduction
• 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
• Introduction
• Chapter 34. Experiences on Image and Video Processing with CUDA and OpenCL
• Chapter 35. Connected Component Labeling in CUDA
• Chapter 36. Image De-Mosaicing
• Introduction
• Chapter 37. Efficient Automatic Speech Recognition on the GPU
• Chapter 38. Parallel LDPC Decoding
• Chapter 39. Large-Scale Fast Fourier Transform
• Introduction
• 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
• Index