GPU Computing Gems Edited by Wen-mei W Hwu
August 26, 2010
[MK] Andrea
 |
Morgan Kaufmann is excited to announce the forthcoming publication of GPU Computing Gems edited by Wen-mei W. Hwu, due out in January 2011. |
About the Book
Graphics Processing Units (GPUs) are designed to be parallel – having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for any computationally-intense operation – not just for graphics. If you’re facing the challenge of programming systems to effectively use these massively parallel processors to achieve efficiency and performance goals, GPU Computing Gems provides a wealth of tested, proven GPU techniques. Learn from the leading researchers in concurrent programming, who have gathered their insights and experience in one volume under the guidance of NVIDIA and GPU expert Wen-mei Hwu.
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 utilize NVIDIA’s CUDA parallel computing architecture, the most widely-adopted GPU programming tool
- Offers insights and ideas as well as practical “hands-on” skills you can immediately put to use
Who should read this book?
Computer programmers, software engineers, hardware engineers, and computer science students, among others.
About the Editor
Wen-mei W. Hwu, co-author of Programming Massively Parallel Processors, is the Walter J. (“Jerry”) Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign. From 1997 to 1999, Dr. Hwu served as the chairman of the Computer Engineering Program at the University of Illinois. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley. His research interests are in the areas of architecture, implementation, and software for high-performance computer systems. He is the director of the OpenIMPACT project, which has delivered new compiler and computer architecture technologies to the computer industry since 1987. He also serves as the Soft Systems Theme leader of the MARCO/DARPA Gigascale Silicon Research Center (GSRC) and on the Executive Committees of both the GSRC and the MARCO/DARPA Center for Circuit and System Solutions. For his contributions to the areas of compiler optimization and computer architecture, he received the 1993 Eta Kappa Nu Outstanding Young Electrical Engineer Award, the 1994 Xerox Award for Faculty Research, the 1994 University Scholar Award of the University of Illinois, the 1997 Eta Kappa Nu Holmes MacDonald Outstanding Teaching Award, the 1998 ACM SigArch Maurice Wilkes Award, the 1999 ACM Grace Murray Hopper Award, the 2001 Tau Beta Pi Daniel C. Drucker Eminent Faculty Award. He served as the Franklin Woeltge Distinguished Professor of Electrical and Computer Engineering from 2000 to 2004. He is a fellow of IEEE and ACM.
Table of Contents
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
ISBN: 9780123849885 | View in bookstore
I am looking forward to read this book. Look at the table of contents, it shows that the author has put lot of knowledge in the book and surely this will help many GPU developers and reseachers.
Thanks Professor Wen-mei W. Hwu