Hands-On GPU：Accelerated Computer Vision with OpenCV and CUDA

封面

Title Page

Copyright and Credits

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Packt Upsell

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Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Code in Action

Conventions used

Get in touch

Reviews

Introducing CUDA and Getting Started with CUDA

Technical requirements

Introducing CUDA

Parallel processing

Introducing GPU architecture and CUDA

CUDA architecture

CUDA applications

CUDA development environment

CUDA-supported GPU

NVIDIA graphics card driver

Standard C compiler

CUDA development kit

Installing the CUDA toolkit on all operating systems

Windows

Linux

Mac

A basic program in CUDA C

Steps for creating a CUDA C program on Windows

Steps for creating a CUDA C program on Ubuntu

Summary

Questions

Parallel Programming using CUDA C

Technical requirements

CUDA program structure

Two-variable addition program in CUDA C

A kernel call

Configuring kernel parameters

CUDA API functions

Passing parameters to CUDA functions

Passing parameters by value

Passing parameters by reference

Executing threads on a device

Accessing GPU device properties from CUDA programs

General device properties

Memory-related properties

Thread-related properties

Vector operations in CUDA

Two-vector addition program

Comparing latency between the CPU and the GPU code

Elementwise squaring of vectors in CUDA

Parallel communication patterns

Map

Gather

Scatter

Stencil

Transpose

Summary

Questions

Threads Synchronization and Memory

Technical requirements

Threads

Memory architecture

Global memory

Local memory and registers

Cache memory

Thread synchronization

Shared memory

Atomic operations

Constant memory

Texture memory

Dot product and matrix multiplication example

Dot product

Matrix multiplication

Summary

Questions

Advanced Concepts in CUDA

Technical requirements

Performance measurement of CUDA programs

CUDA Events

The Nvidia Visual Profiler

Error handling in CUDA

Error handling from within the code

Debugging tools

Performance improvement of CUDA programs

Using an optimum number of blocks and threads

Maximizing arithmetic efficiency

Using coalesced or strided memory access

Avoiding thread divergence

Using page-locked host memory