CUDA Application Design and Development
November 3, 2011
SteveH
 |
CUDA Application Design and Development by Rob Farber just published!
Review the full Table of Contents below. |
About the Book
As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and determining application lifespan.
The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries.
Using an approach refined in a series of well-received articles at Dr Dobb’s Journal, author Rob Farber takes the reader step-by-step from fundamentals to implementation, moving from language theory to practical coding.
Key Features
- Includes multiple examples building from simple to more complex applications in four key areas: machine learning, visualization, vision recognition, and mobile computing
- Addresses the foundational issues for CUDA development: multi-threaded programming and the different memory hierarchy
- Includes teaching chapters designed to give a full understanding of CUDA tools, techniques and structure
- Presents CUDA techniques in the context of the hardware they are implemented on as well as other styles of programming that will help readers bridge into the new material
Quotes:
“The most important thing that this book will offer is the application specific examples and the additional detail of how to optimize CUDA for different application areas. This book will also be valuable as an alternative to the existing textbooks, since it is written by a user with an application perspective.
- David Kirk, author of Programming Massively Parallel Processors and former NVIDIA Chief Scientist
Table of Contents:
CHAPTER 1: First Programs and How to Think in CUDA
Source Code and Wiki
Distinguishing CUDA from Conventional Programming with a Simple Example
Choosing a CUDA API
Some Basic CUDA Concepts
Understanding Our First Runtime Kernel
Three Rules of GPGPU Programming
Big-O Considerations and Data Transfers
CUDA and Amdahl’s Law
Data and Task Parallelism
Hybrid Execution: Using Both CPU and GPU Resources
Regression Testing and Accuracy
Silent Errors
Introduction to Debugging
UNIX Debugging
Windows Debugging with Parallel Nsight
Summary
CHAPTER 2: CUDA for Machine Learning and Optimization
Modeling and Simulation
Machine Learning and Neural Networks
XOR: An Important Nonlinear Machine-Learning Problem
Performance Results on XOR
Performance Discussion
Summary
The C++ Nelder-Mead Template
CHAPTER 3: The CUDA Tool Suite: Profiling a PCA/NLPCA
Functor
PCA and NLPCA
Obtaining Basic Profile Information
Gprof: A Common UNIX Profiler
The NVIDIA Visual Profiler: Computeprof
Parallel Nsight for Microsoft Visual Studio
Tuning and Analysis Utilities (TAU)
Summary
CHAPTER 4: The CUDA Execution Model
GPU Architecture Overview
Warp Scheduling and TLP
ILP: Higher Performance at Lower Occupancy
Little’s Law
CUDA Tools to Identify Limiting Factors
Summary
CHAPTER 5: CUDA Memory
The CUDA Memory Hierarchy
GPU Memory
L2 Cache
L1 Cache
CUDA Memory Types
Global Memory
Summary
CHAPTER 6: Efficiently Using GPU Memory
Reduction
Utilizing Irregular Data Structures
Sparse Matrices and the CUSP Library
Graph Algorithms
SoA, AoS, and Other Structures
Tiles and Stencils
Summary
CHAPTER 7: Techniques to Increase Parallelism
CUDA Contexts Extend Parallelism
Streams and Contexts
Out-of-Order Execution with Multiple Streams
Tying Data to Computation
Summary
CHAPTER 8: CUDA for All GPU and CPU Applications
Pathways from CUDA to Multiple Hardware Backends
Accessing CUDA from Other Languages
Libraries
CUBLAS
CUFFT
Summary
CHAPTER 9: Mixing CUDA and Rendering
OpenGL
GLUT
Introduction to the Files in the Framework
Summary
CHAPTER 10: CUDA in a Cloud and Cluster Environments
The Message Passing Interface (MPI)
How MPI Communicates
Bandwidth
Balance Ratios
Considerations for Large MPI Runs
Cloud Computing
A Code Example
Summary
CHAPTER 11: CUDA for Real Problems
Working with High-Dimensional Data
PCA/NLPCA
Force-Directed Graphs
Monte Carlo Methods
Molecular Modeling
Quantum Chemistry
Interactive Workflows
A Plethora of Projects
Summary
CHAPTER 12: Application Focus on Live Streaming Video
Topics in Machine Vision
FFmpeg
TCP Server
Contents ix
Live Stream Application
The simpleVBO.cpp File
The callbacksVBO.cpp File
Building and Running the Code
The Future
Summary
ISBN: 9780123884268 | View in bookstore
