book

GPU Computing Gems Emerald Edition

Name: GPU Computing Gems Emerald Edition
Author: Wen-mei W. Hwu
ISBN: 9780123849892

by Wen-mei W. Hwu

January 2011

Intermediate to advanced

886 pages

28h 35m

English

Morgan Kaufmann

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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
1.1. Introduction, Problem Statement, and Context1.2. Core Method1.3. Algorithms, Implementations, and Evaluations1.4. Final Evaluation1.5. Future Directions
Chapter 2. Large-Scale Chemical Informatics on GPUs
2.1. Introduction, Problem Statement, and Context2.2. Core Methods2.3. Gaussian Shape Overlay: Parallelization and Arithmetic Optimization2.4. LINGO: Algorithmic Transformation and Memory Optimization2.5. Final Evaluation2.6. Future Directions
Chapter 3. Dynamical Quadrature Grids
3.1. Introduction3.2. Core Method3.3. Implementation3.4. Performance Improvement3.5. Future Work

Chapter 4. Fast Molecular Electrostatics Algorithms on GPUs
4.1. Introduction, Problem Statement, and Context4.2. Core Method4.3. Algorithms, Implementations, and Evaluations4.4. Final Evaluation4.5. Future Directions
Chapter 5. Quantum Chemistry
5.1. Problem Statement5.2. Core Technology and Algorithm5.3. The Key Insight on the Implementation—the Choice of Building Blocks5.4. Final Evaluation and Benefits5.5. Conclusions and Future Directions
Chapter 6. An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
6.1. Introduction, Problem Statement, and Context6.2. Core Methods6.3. Algorithms and Implementations6.4. Evaluation and Validation of Results, Total Benefits, and Limitations6.5. Future Directions
Chapter 7. Leveraging the Untapped Computation Power of GPUs
7.1. Background and Problem Statement7.2. Flux Calculation and Aggregation7.3. The GRASSY Platform7.4. Initial Testing7.5. Impact and Future Directions
Chapter 8. Black Hole Simulations with CUDA
8.1. Introduction8.2. The Post-Newtonian Approximation8.3. Numerical Algorithm8.4. GPU Implementation8.5. Performance Results8.6. GPU Supercomputing Clusters8.7. Statistical Results for Black Hole Inspirals8.8. Conclusion
Chapter 9. Treecode and Fast Multipole Method for N-Body Simulation with CUDA
9.1. Introduction9.2. Fast N-Body Simulation9.3. CUDA Implementation of the Fast N-Body Algorithms9.4. Improvements of Performance9.5. Detailed Description of the GPU Kernels9.6. Overview of Advanced Techniques9.7. Conclusions
Chapter 10. Wavelet-Based Density Functional Theory Calculation on Massively Parallel Hybrid Architectures
10.1. Introduction, Problem Statement, and Context10.2. Core Method10.3. Algorithms, Implementations, and Evaluations10.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations10.5. Conclusions and Future Directions
Introduction
Chapter 11. Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm
11.1. Introduction, Problem Statement, and Context11.2. Core Method11.3. CUDA implementation of the SW algorithm for identification of homologous proteins11.4. Discussion11.5. Final Evaluation
Chapter 12. Massive Parallel Computing to Accelerate Genome-Matching
12.1. Introduction, Problem Statement, and Context12.2. Core Methods12.3. Algorithms, Implementations, and Evaluations12.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations12.5. Future Directions
Chapter 13. GPU-Supercomputer Acceleration of Pattern Matching
13.1. Introduction, Problem Statement, and Context13.2. Core Method13.3. Algorithms, Implementations, and Evaluations13.4. Final Evaluation13.5. Future Direction
Chapter 14. GPU Accelerated RNA Folding Algorithm
14.1. Problem Statement14.2. Core Method14.3. Algorithms, Implementations, and Evaluations14.4. Final Evaluation14.5. Future Directions
Chapter 15. Temporal Data Mining for Neuroscience
15.1. Introduction15.2. Core Methodology15.3. GPU Parallelization: Algorithms and Implementations15.4. Experimental Results15.5. Discussion
Introduction
Chapter 16. Parallelization Techniques for Random Number Generators
16.1. Introduction16.2. L'Ecuyer's Multiple Recursive Generator MRG32k3a16.3. Sobol Generator16.4. Mersenne Twister MT1993716.5. Performance Benchmarks
Chapter 17. Monte Carlo Photon Transport on the GPU
17.1. Physics of Photon Transport17.2. Photon Transport on the GPU17.3. The Complete System17.4. Results and Evaluation17.5. Future Directions
Chapter 18. High-Performance Iterated Function Systems
18.1. Problem Statement and Mathematical Background18.2. Core Technology18.3. Implementation18.4. Final Evaluation18.5. Conclusion
Introduction
Chapter 19. Large-Scale Machine Learning
19.1. Introduction19.2. Core Technology19.3. GPU Algorithm and Implementation19.4. Improvements of Performance19.5. Conclusions and Future Work
Chapter 20. Multiclass Support Vector Machine
20.1. Introduction, Problem Statement, and Context20.2. Core Method20.3. Algorithms, Implementations, and Evaluations20.4. Final Evaluation20.5. Future Direction
Chapter 21. Template-Driven Agent-Based Modeling and Simulation with CUDA
21.1. Introduction, Problem Statement, and Context21.2. Final Evaluation and Validation of Results21.3. Conclusions, Benefits and Limitations, and Future Work
Chapter 22. GPU-Accelerated Ant Colony Optimization
22.1. Introduction, Problem Statement, and Context22.2. Core Method22.3. Algorithms, Implementations, and Evaluations22.4. Final Evaluation22.5. Future Direction
Introduction
Chapter 23. High-Performance Gate-Level Simulation with GP-GPUs
23.1. Introduction23.2. Simulator Overview23.3. Compilation and Simulation23.4. Experimental Results23.5. Future Directions
Chapter 24. GPU-Based Parallel Computing for Fast Circuit Optimization
24.1. Introduction, Problem Statement, and Context24.2. Core Method24.3. Algorithms, Implementations, and Evaluations24.4. Final Evaluation24.5. Future Direction
Introduction
Chapter 25. Lattice Boltzmann Lighting Models
25.1. Introduction, Problem Statement, and Context25.2. Core Methods25.3. Algorithms, Implementation, and Evaluation25.4. Final Evaluation25.5. Future Directions25.6. Derivation of the Diffusion Equation
Chapter 26. Path Regeneration for Random Walks
26.1. Introduction26.2. Path Tracing as Case Study26.3. Random Walks in Path Tracing26.4. Implementation Details26.5. Results26.6. Discussion
Chapter 27. From Sparse Mocap to Highly Detailed Facial Animation
27.1. System Overview27.2. Background27.3. Core Technology and Algorithms27.4. Future Directions
Chapter 28. A Programmable Graphics Pipeline in CUDA for Order-Independent Transparency
28.1. Introduction, Problem Statement, and Context28.2. Core Method28.3. Algorithms, Implementations, and Evaluations28.4. Final Evaluation28.5. Future Direction
Introduction
Chapter 29. Fast Graph Cuts for Computer Vision
29.1. Introduction, Problem Statement, and Context29.2. Core Method29.3. Algorithms, Implementations, and Evaluations29.4. Final evaluation and validation of results29.5. Multilabel Graph Cuts
Chapter 30. Visual Saliency Model on Multi-GPU
30.1. Introduction30.2. Visual Saliency Model30.3. GPU Implementation30.4. Results30.5. Conclusion
Chapter 31. Real-Time Stereo on GPGPU Using Progressive Multiresolution Adaptive Windows
31.1. Introduction, Problem Statement, and Context31.2. Core Method
Chapter 32. Real-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
32.1. Introduction32.2. Methods32.3. Implementation32.4. Results and Discussion32.5. Conclusion and Future Work
Chapter 33. Haar Classifiers for Object Detection with CUDA
33.1. Introduction33.2. Viola-Jones Object Detection Retrospective33.3. Object Detection Pipeline with NVIDIA CUDA33.4. Benchmarking and Implementation Details33.5. Future Direction33.6. Conclusion
Introduction
Chapter 34. Experiences on Image and Video Processing with CUDA and OpenCL
34.1. Introduction, Problem Statement, and Background34.2. Core Technology or Algorithm34.3. Key Insights from Implementation and Evaluation34.4. Final Evaluation
Chapter 35. Connected Component Labeling in CUDA
35.1. Introduction35.2. Core Algorithm35.3. CUDA Algorithm and Implementation35.4. Final Evaluation and Results
Chapter 36. Image De-Mosaicing
36.1. Introduction, Problem Statement, and Context36.2. Core Method36.3. Algorithms, Implementations, and Evaluations36.4. Final Evaluation
Introduction
Chapter 37. Efficient Automatic Speech Recognition on the GPU
37.1. Introduction, Problem Statement, and Context37.2. Core Methods37.3. Algorithms, Implementations, and Evaluations37.4. Conclusion and Future Directions
Chapter 38. Parallel LDPC Decoding
38.1. Introduction, Problem Statement, and Context38.2. Core Technology38.3. Algorithms, Implementations, and Evaluations38.4. Final Evaluation38.5. Future Directions
Chapter 39. Large-Scale Fast Fourier Transform
39.1. Introduction39.2. Memory Hierarchy of GPU Clusters39.3. Large-Scale Fast Fourier Transform39.4. Algebraic Manipulation of Array Dimensions39.5. Performance Results39.6. Conclusion and Future Work
Introduction
Chapter 40. GPU Acceleration of Iterative Digital Breast Tomosynthesis
40.1. Introduction40.2. Digital Breast Tomosynthesis40.3. Accelerating Iterative DBT using GPUs40.4. Conclusions
Chapter 41. Parallelization of Katsevich CT Image Reconstruction Algorithm on Generic Multi-Core Processors and GPGPU
41.1. Introduction, Problem, and Context41.2. Core Methods41.3. Algorithms, Implementations, and Evaluations41.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations41.5. Related Work41.6. Future Directions41.7. Summary
Chapter 42. 3-D Tomographic Image Reconstruction from Randomly Ordered Lines with CUDA
42.1. Introduction42.2. Core Methods42.3. Implementation42.4. Evaluation and Validation of Results, Total Benefits, and Limitations42.5. Future Directions
Chapter 43. Using GPUs to Learn Effective Parameter Settings for GPU-Accelerated Iterative CT Reconstruction Algorithms
43.1. Introduction, Problem Statement, and Context43.2. Core Method(s)43.3. Algorithms, Implementations, and Evaluations43.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations43.5. Future Directions
Chapter 44. Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation
44.1. Introduction44.2. Core Method: Advanced Image Reconstruction Toolbox for MRI44.3. MRI Reconstruction Algorithms and Implementation on GPUs44.4. Final Results and Evaluation44.5. Conclusion and Future Directions
Chapter 45. ℓ1 Minimization in ℓ1-SPIRiT Compressed Sensing MRI Reconstruction
45.1. Introduction, Problem Statement, and Context45.2. Core Methods (High Level Description)45.3. Algorithms, Implementations, and Evaluations (Detailed Description)45.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations45.5. Discussion and Conclusion
Chapter 46. Medical Image Processing Using GPU-Accelerated ITK Image Filters
46.1. Introduction46.2. Core Methods46.3. Implementation46.4. Results46.5. Future Directions46.6. Acknowledgments
Chapter 47. Deformable Volumetric Registration Using B-Splines
47.1. Introduction47.2. An Overview of B-Spline Registration47.3. Implementation Details47.4. Results47.5. Conclusions
Chapter 48. Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs
48.1. Introduction, Problem Statement, and Context48.2. Core Methods48.3. Algorithms, Implementations, and Evaluations48.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations48.5. Future Directions
Chapter 49. GPU-Accelerated Brain Connectivity Reconstruction and Visualization in Large-Scale Electron Micrographs
49.1. Introduction49.2. Core Methods49.3. Implementation49.4. Results49.5. Future Directions
Chapter 50. Fast Simulation of Radiographic Images Using a Monte Carlo X-Ray Transport Algorithm Implemented in CUDA
50.1. Introduction, Problem Statement, and Context50.2. Core Methods50.3. Algorithms, Implementations, and Evaluations50.4. Final Evaluation and Validation of Results, Total Benefits, and Limitations50.5. Future Directions
Index

Overview

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:

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