CUDA应用设计与开发指南

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"CUDA Application Design and Development.pdf" 是一本关于CUDA应用设计与开发的新教程，由Rob Farber撰写，由Morgan Kaufmann (Elsevier的一个印记) 出版。本书涵盖了GPU编程的基础以及CUDA（Compute Unified Device Architecture）的高级概念，旨在帮助读者深入理解和实践CUDA在高性能计算和并行计算中的应用。 CUDA是NVIDIA推出的一种编程模型，它允许开发者利用图形处理单元（GPU）的强大计算能力来执行通用计算任务，而不仅仅局限于图形渲染。在CUDA中，GPU被视为一个并行计算平台，拥有大量的线程和核心，能够同时处理大量数据，从而显著提高计算效率。该书可能涉及的知识点包括： 1. **CUDA编程基础**：介绍CUDA编程环境的设置，包括CUDA SDK的安装、CUDA C/C++编程语言基础，以及如何编写第一个CUDA程序（通常称为“Hello World”程序）。 2. **CUDA架构**：详细讲解CUDA架构，包括多核GPU的组织结构，如流式多处理器（SMs）、全局内存、共享内存、寄存器等。 3. **线程和块的概念**：CUDA中的并行执行是通过线程和线程块实现的。理解如何组织和同步这些线程对于高效编程至关重要。 4. **CUDA内存管理**：包括全局内存、共享内存、常量内存和纹理内存的特性及使用方法，以及如何优化内存访问以减少延迟。 5. **CUDA并行计算策略**：探讨如何有效地分配工作负载到GPU，如线程块大小的选择，计算密度的优化，以及如何避免数据竞争。 6. **错误处理和调试**：CUDA程序的调试技巧，包括如何利用CUDA Profiler进行性能分析和错误检测。 7. **CUDA并行算法设计**：涵盖如何将串行算法转换为并行算法，例如矩阵乘法、傅里叶变换和其他科学计算中的常见操作。 8. **性能优化**：讨论如何最大化GPU的计算效率，如使用流式处理，优化内存传输，以及利用CUDART库函数。 9. **CUDA在实际应用中的案例**：可能会包含图像处理、物理模拟、机器学习和深度学习等领域中CUDA的实际应用示例。 10. **CUDA与OpenMP、MPI等并行计算框架的比较**：对比CUDA与其他并行计算工具的优势和适用场景。本书适合有C/C++编程基础，希望进入GPU编程领域的开发者，或是希望提升现有CUDA项目性能的工程师阅读。通过深入学习，读者可以掌握利用CUDA进行高性能计算的关键技能，并能够创建高效的GPU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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CUDA应用设计与开发指南

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