CUDA编程实践：大规模并行处理指南

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"CUDA Application Design and Development" CUDA（Compute Unified Device Architecture）是由NVIDIA公司推出的一种并行计算平台和编程模型，专门针对GPU（图形处理器）进行高性能计算设计。这本书《CUDA Application Design and Development》由Rob Farber撰写，是CUDA开发者的必备参考书籍，尤其对于大规模并行处理有着深入的讲解和实践指导。在现代科学计算、机器学习、深度学习等领域，CUDA因其高效利用GPU的并行计算能力而备受青睐。通过CUDA，开发者可以直接用C/C++或者Python等高级语言编写程序，将计算任务分发到GPU上，从而实现比传统CPU更快的数据处理速度。本书涵盖了以下关键知识点： 1. **CUDA编程基础**：介绍CUDA编程环境的设置，包括安装CUDA工具包、配置编译器以及理解CUDA编程模型，如线程块、网格、共享内存等基本概念。 2. **GPU架构**：详细解析NVIDIA GPU的硬件架构，包括流处理器（Streaming Multiprocessors）、内存层次结构（全局内存、共享内存、常量内存和纹理内存）以及内存带宽优化策略。 3. **并行编程模型**：深入探讨如何有效地利用并行性，包括线程同步、内存访问模式优化、原子操作等，以提高代码的效率和可扩展性。 4. **性能调优**：讲解如何分析和优化CUDA程序的性能，使用NVIDIA提供的性能分析工具如nvprof，以及各种性能指标和最佳实践。 5. **CUDA C++编程技巧**：涵盖CUDA C++的高级特性，如模板、动态并行ism、设备函数等，帮助开发者写出更高效、可维护的代码。 6. **应用案例**：书中可能包含多个实际应用案例，例如图像处理、物理模拟、数值计算等，展示CUDA在不同领域的应用和优势。 7. **错误处理与调试**：介绍CUDA程序中的错误检测和调试方法，帮助开发者解决在开发过程中遇到的问题。 8. **最新技术更新**：由于CUDA技术不断更新，书中可能涉及当时的最新CUDA版本特性，如CUDA C++11支持、CUDA Graphs等。通过阅读本书，读者不仅可以掌握CUDA编程的基本技能，还能了解到并行计算的设计原则和最佳实践，有助于提升开发大型并行应用程序的能力。书中的实例和实践经验对提升实际项目中的性能至关重要，是任何想要深入CUDA开发的IT专业人员的宝贵资源。

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编程实践：大规模并行处理指南

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