CUDA编程：设计与开发实战

5星 · 超过95%的资源需积分: 10 161 浏览量更新于2024-07-27 收藏 6.92MB PDF 举报

"CUDA Application Design and Development" 是一本由 Rob Farber 撰写的书籍，专注于CUDA技术在应用设计和开发中的实践。CUDA是NVIDIA公司推出的一种并行计算平台和编程模型，允许开发者利用GPU（图形处理单元）的强大计算能力进行高性能计算。这本书的描述暗示了作者在撰写过程中得到了家人、朋友以及ICHEC（爱尔兰高端计算中心）的支持，他们帮助作者完成了书稿的校对和最终定稿。此外，NVIDIA公司的同事和朋友对于CUDA革命的推动也得到了作者的感谢，他们对CUDA技术的发展起到了关键作用。在CUDA应用设计和开发的部分内容中，虽然没有提供具体的章节或技术细节，但可以推测书中可能涵盖了以下关键知识点： 1. **CUDA编程基础**：包括CUDA编程环境的设置，CUDA C/C++编程语言的基础，如内核函数、全局内存、共享内存和线程块的概念。 2. **并行计算理论**：可能涉及并行计算的基本概念，如并行度、同步、通信和数据并行性，以及如何在CUDA中实现这些概念。 3. **CUDA架构**：讲解CUDA GPU的硬件架构，如流处理器（CUDA Cores）、纹理内存、常量内存等，以及如何优化代码以充分利用这些特性。 4. **性能优化**：探讨如何通过代码调整、内存管理、计算效率提升等手段来提高CUDA程序的运行速度。 5. **错误检查和调试**：介绍CUDA程序的调试工具和技巧，如nvcc编译器的使用，CUDA Profiler的分析方法，以及如何处理和避免CUDA运行时错误。 6. **实际应用案例**：可能包含各种领域如物理模拟、图像处理、科学计算等的实际应用示例，展示CUDA在不同场景下的应用方法和优势。 7. **多GPU编程**：讨论如何在多GPU系统上进行并行编程，包括CUDA设备的选择、数据迁移和负载均衡。 8. **最新CUDA版本特性**：书中可能会提及当时最新的CUDA版本特性，以及与前一版本的差异和改进。 9. **最佳实践和指导原则**：为开发者提供编写高效CUDA代码的最佳实践和建议。 10. **资源和社区**：可能提供CUDA开发者社区、论坛、文档和工具的链接，帮助读者进一步学习和解决问题。《CUDA Application Design and Development》是一本全面介绍CUDA技术的实战指南，旨在帮助开发者理解和掌握CUDA编程，从而利用GPU的强大计算能力解决复杂计算问题。书中涵盖了从基础到高级的诸多主题，适合有一定编程基础并希望进入GPU计算领域的读者阅读。

In addition, this book will show you how to use CUDA to develop

applications that can run on multicore processors, making CUDA a viable

choice for all application development. No GPU required!

Not concerned with just syntax and API calls, the material in this book

covers the thought behind the design of CUDA, plus the architectural rea-

sons why GPGPU hardware can perform so spectacularly. Various guidelines

and caveats will be covered so that you can write concise, readable, and

maintainable code. The focus is on the latest CUDA 4.x release.

Working code is provided that can be compiled and modified because play-

ing with and adapting code is an essential part of the learning process. The

examples demonstrate how to get high-performance from the Fermi architec-

ture (NVIDIA 20-series) of GPGPUS because the inte ntion is not just to get

code working but also to show you how to write efficient code. Those with

older GPGPUs will benefit from this book, as the examples will compile

and run on all CUDA-enabled GPGPUs. Where appropriate, this book will

reference text from my extensive Doctor Dobb’s Journal series of CUDA tutor-

ials to highl ight improvements over previous versions of CUDA and to pro-

vide insight on how to achi eve good performance across multiple

generations of GPGPU architectures.

Teach ing materials, additional examples, and reader comments are available

on the http://gpucomputing.net wiki. Any of the following URLs will access

the wiki:

■

My name: http://gpucomputing.net/RobFarber.

■

The title of this book as one word: http://gpucomputing.net/

CUDAapplicationdesignanddevelopment.

■

The name of my series: http://gpucomputing.net/

supercomputingforthemasses.

Those who purchase the book can downl oad t he sour ce co de for the

examples at http://booksite.mkp.com/9780123884268.

To accomplish these goals, the book is organized as follows:

Chapter 1. Introduces basic CUDA concepts and the tools needed to

build and debug CUDA applications. Simple examples are provided that

demonstrates both the thrust C++ and C runtime APIs. Three simple

rules for high-performance GPU programming are intro duced.

Chapter 2. Using only techniques introduced in Chapter 1, this

chapter provides a complete, general-purpose machine-learning and

optimization framework that can run 341 times faster than a single

core of a conventional processor. Core concepts in machine learning

xiv Preface

and numerical optimization are also covered, which will be of interest

to those who desire the domain knowledge as well as the ability to

program GPUs.

Chapter 3. Profiling is the focus of this chapter, as it is an essential skill

in high-perfo rmance programming. The CUDA profiling tools are

introduced and applied to the real-world example from Chapter 2. Some

surprising bottlenecks in the Thrust API are uncovered. Introductory

data-mining techniques are discussed and data-mining functors for both

Principle Components Analysis and Nonlinear Principle Components

Analysis are provided, so this chapter should be of interest to users as

well as programmers.

Chapter 4. The CUDA execution model is the topic of this chapter.

Anyone who wishes to get peak performance from a GPU must

understand the concepts covered in this chapter. Examples and profiling

output are provided to help understand both what the GPU is doing

and how to use the existing tools to see what is happening.

Chapter 5. CUDA provides several types of memory on the GPU. Each

type of memory is discussed, along with the advantages and

disadvantages.

Chapter 6. With over three orders-of-magnitude in performance difference

between the fastest and slowest GPU memory, efficiently using memory

on the GPU is the only path to high performance. This chapter discusses

techniques and provides profiler output to help you understand and

monitor how efficiently your applications use memory. A general functor-

based example is provided to teach how to write your own generic

methods like the Thrust API.

Chapter 7. GPUs provide multiple forms of parallelism, including

multiple GPUs, asynchronous kernel execution, and a Unified Virtual

Address (UVA) space. This chapter provides examples and profiler

output to understand and utilize all forms of GPU parallelism.

Chapter 8. CUDA has matured to become a viable platform for all

application development for both GPU and multicore processors. Pathways

to multiple CUDA backends are discussed, and examples and profiler

output to effectively run in heterogeneous multi-GPU environments are

provided. CUDA libraries and how to interface CUDA and GPU computing

with other high-level languages like Python, Java, R, and FORTRAN are

covered.

Chapter 9. With the focus on the use of CUDA to accelerate computational

tasks, it is easy to forget that GPU technology is also a splendid platform

for visualization. This chapter discusses primitive restart and how it can

dramatically accelerate visualization and gaming applications. A complete

working example is provided that allows the reader to create and fly

Preface xv

around in a 3D world. Profiler output is used to demonstrate why

primitive restart is so fast. The teaching framework from this chapter is

extended to work with live video streams in Chapter 12.

Chapter 10. To teach scalability, as well as performance, the example

from Chapter 3 is extended to use MPI (Message Passing Interface). A

variant of this example code has dem onstrated near-linear scalability to

500 GPGPUs (with a peak of over 500,000 single-precision gigaflops)

and delivered over one-third petaflop (10

floating-point operations

per second) using 60,000 x86 processing cores.

Chapter 11. No book can cover all aspects of the CUDA tidal wave. This

is a survey chapter that points the way to other projects that provide free

working source code for a variety of techniques, including Support

Vector Machines (SVM), Multi-Dimensional Scaling (MDS), mutual

information, force-directed graph layout, molecular modeling, and

others. Knowledge of these projects—and how to interface with other

high-level lan guages, as discussed in Chapter 8—will help you mature as

a CUDA developer.

Chapter 12. A working real-time video streaming example for vision

recognition based on the visualization framework in Chapter 9 is

provided. All that is needed is an inexpensive webcam or a video file so

that you too can work with real-time vision recognition. This example

was designed for teaching, so it is easy to modify. Robotics, augmented

reality games, and data fusion for heads-up displays are obv ious

extensions to the working example and technology discussion in this

chapter.

Learning to think about and pro gram in CUDA (and GPGPUs) is a wonderful

way to have fun and open new opportunities. However, performance is the

ultimate reason for using GPGPU technology, and as one of my university

professors used to say, “The proof of the pudding is in the tasting.” Figure 1

illustrates the performance of the top 100 applications as reported on the

NVIDIA CUDA Showcase

as of July 12, 2011. They demonstrate the wide

variety of applications that GPGPU technology can accelerate by two or more

orders of magnitude (100-times) over multi-core processors, a s reported in

the peer-reviewed scientific literature and by commercial entities. It is worth

taking time to look over these showcased applications, as many of them pro-

vide freely downloadable source code and libraries.

GPGPU technology is a disruptive technology that has redefined how

computation occurs. As NVIDIA notes, “from super phones to supercompu-

ters.” This technology has arri ved during a perfect storm o f opportunities, as

traditional multicore processors can no longer achieve significant speedups

http://developer.nvidia.com/cuda-action-research-apps.

xvi Preface

剩余323页未读，继续阅读

neversdop

粉丝: 0
资源: 3

CUDA编程：设计与开发实战

Cuda Application Design and Development 2011

CUDA.Application.Design.and.Development,.Farber,.MK,.2011

CUDA应用设计与开发指南

CUDA应用设计与开发实战

CUDA编程入门指南：300页深度解析

CUDA编程实践：大规模并行处理指南

【Network Architecture】: Delving into DCGAN and Its Variants: Exploring the Diversity and Potential...

推荐cuda编程的热门书籍

基于 C++构建 Qt 实现的 GDAL 与 PROJ4 的遥感图像处理软件课程设计

【java毕业设计】娜娜服装企业物流管理系统源码（完整前后端+说明文档+LW）.zip

最新资源