CUDA应用设计与开发：英文版PDF

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"《CUDA应用设计与开发》是2011年由Rob Farber撰写的一本英文版PDF书籍，专注于CUDA技术在GPU开发中的应用。本书由Morgan Kaufmann（Elsevier的一个印记）出版，涉及CUDA编程、GPU计算以及相关的软件开发实践。" 《CUDA应用设计与开发》一书深入探讨了CUDA(CUDA是Compute Unified Device Architecture的缩写)平台，这是一个由NVIDIA公司推出的并行计算架构，允许程序员直接利用GPU(图形处理器)的强大计算能力进行通用计算。CUDA提供了C/C++编程接口，让开发者能够编写高效能的并行代码，尤其适合处理大规模数据并行计算任务，如物理模拟、图像处理和机器学习等领域。在书中，作者Rob Farber引导读者了解CUDA编程的基础，包括CUDA编程模型、设备内存管理、线程同步、内核函数优化等关键概念。他还详细解释了如何利用CUDA SDK (Software Development Kit) 和CUDA工具来调试和性能分析。此外，书中可能还涵盖了CUDA的最新版本特性，例如统一内存、动态并行化和流式多处理器架构等。 CUDA GPU开发的关键知识点包括： 1. **CUDA编程模型**：CUDA编程模型基于多线程块和网格，线程块内的线程可以高效通信和同步，而不同线程块之间通过全局内存交互。 2. **设备内存系统**：CUDA有多种类型的内存，如全局内存、共享内存、常量内存和纹理内存，理解它们的特性和使用场景至关重要。 3. **并行计算和线程同步**：掌握如何有效地组织和调度线程，以及如何在GPU上实现同步是CUDA编程的核心挑战。 4. **内核函数**：内核函数是运行在GPU上的函数，开发者需要优化这些函数以最大化计算效率。 5. **性能分析和调试**：使用CUDA Profiler和其他工具对程序进行性能分析，找出瓶颈并优化代码。 6. **CUDA库和API**：CUDA提供了一系列预构建的库，如cuBLAS（用于线性代数）、cuFFT（用于快速傅里叶变换）和cuDNN（用于深度神经网络）等，熟悉这些库能简化开发过程。该书不仅适合有一定C/C++基础的初学者，也适合有经验的开发者深入学习CUDA技术。通过阅读这本书，读者可以提升在GPU加速计算领域的专业技能，理解并掌握如何利用CUDA实现高效的并行计算应用程序。

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

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