GPU Computing Gems, Emerald Edition

  • 17h 49m
  • Wen-mei W. Hwu (ed)
  • Elsevier Science and Technology Books, Inc.
  • 2011

Graphics Processing Units (GPUs) are designed to be parallel having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for many computationally-intense applications 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.

Different application domains often pose similar algorithm problems, and researchers from diverse application domains often develop similar algorithmic strategies.GPU Computing Gems 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.

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.

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, video and image processing.

Features

  • A snapshot of the state of GPU computing in ten critical domains, edited by Wen-mei W. Hwu with experts from NVIDIA Corporation and instructors from leading GPU programs worldwide
  • Many examples leverage 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

About the Author

Wen-Mei W. Hwu is the co-author of Programming Massively Parallel Processors and 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.

In this Book

  • GPU-Accelerated Computation and Interactive Display of Molecular Orbitals
  • Large-Scale Chemical Informatics on GPUs
  • Dynamical Quadrature Grids: Applications in Density Functional Calculations
  • Fast Molecular Electrostatics Algorithms on GPUs
  • Quantum Chemistry: Propagation of Electronic Structure on a GPU
  • An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
  • Leveraging the Untapped Computation Power of GPUs: Fast Spectral Synthesis Using Texture Interpolation
  • Black Hole Simulations with CUDA
  • Treecode and Fast Multipole Method for N-Body Simulation with CUDA
  • Wavelet-Based Density Functional Theory Calculation on Massively Parallel Hybrid Architectures
  • Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm
  • Massive Parallel Computing to Accelerate Genome-Matching
  • GPU-Supercomputer Acceleration of Pattern Matching
  • GPU Accelerated RNA Folding Algorithm
  • Temporal Data Mining for Neuroscience
  • Parallelization Techniques for Random Number Generators
  • Monte Carlo Photon Transport on the GPU
  • High-Performance Iterated Function Systems
  • Large-Scale Machine Learning
  • Multiclass Support Vector Machine
  • Template-Driven Agent-Based Modeling and Simulation with CUDA
  • GPU-Accelerated Ant Colony Optimization
  • High-Performance Gate-Level Simulation with GP-GPUs
  • GPU-Based Parallel Computing for Fast Circuit Optimization
  • Lattice Boltzmann Lighting Models
  • Path Regeneration for Random Walks
  • From Sparse Mocap to Highly Detailed Facial Animation
  • A Programmable Graphics Pipeline in CUDA for Order-Independent Transparency
  • Fast Graph Cuts for Computer Vision
  • Visual Saliency Model on Multi-GPU
  • Real-Time Stereo on GPGPU Using Progressive Multiresolution Adaptive Windows
  • Real-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
  • Haar Classifiers for Object Detection with CUDA
  • Experiences on Image and Video Processing with CUDA and OpenCL
  • Connected Component Labeling in CUDA
  • Image De-Mosaicing
  • Efficient Automatic Speech Recognition on the GPU
  • Parallel LDPC Decoding
  • Large-Scale Fast Fourier Transform
  • GPU Acceleration of Iterative Digital Breast Tomosynthesis
  • Parallelization of Katsevich CT Image Reconstruction Algorithm on Generic Multi-Core Processors and GPGPU
  • 3-D Tomographic Image Reconstruction from Randomly Ordered Lines with CUDA
  • Using GPUs to Learn Effective Parameter Settings for GPU-Accelerated Iterative CT Reconstruction Algorithms
  • Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation
  • ℓ1 Minimization in ℓ1-SPIRiT Compressed Sensing MRI Reconstruction
  • Medical Image Processing Using GPU-Accelerated ITK Image Filters
  • Deformable Volumetric Registration Using B-Splines
  • Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs
  • GPU-Accelerated Brain Connectivity Reconstruction and Visualization in Large-Scale Electron Micrographs
  • Fast Simulation of Radiographic Images Using a Monte Carlo X-Ray Transport Algorithm Implemented in CUDA
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