CUDA编程基础教程：GPU通用计算入门

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"CUDA编程入门，是一本针对GPU编程初学者的书籍，包含实例程序，易于理解，专注于CUDA技术在通用GPU编程中的应用。" CUDA（Compute Unified Device Architecture）是NVIDIA公司推出的一种并行计算平台和编程模型，主要用于利用GPU（图形处理器单元）进行高性能计算。这本书“CUDA by Example”旨在引导读者进入CUDA编程的世界，通过实例来讲解这一技术，适合没有或仅有少量GPU编程经验的学习者。 GPU在过去主要被用于图形渲染，但随着技术的发展，它们现在也被广泛应用于科学计算、数据分析、深度学习等领域，因为GPU具有大量的处理核心，能同时执行大量计算任务，从而提供比传统CPU更高的计算性能。本书将涵盖以下关键知识点： 1. **CUDA编程环境设置**：如何安装CUDA工具包，配置开发环境，包括CUDA SDK、编译器nvcc等，以及如何在CUDA支持的系统上运行CUDA程序。 2. **CUDA架构**：介绍GPU的多级内存层次（如全局内存、共享内存、常量内存和纹理内存），流式多处理器（SMs）的工作方式，以及线程组织（线程块和网格）的概念。 3. **CUDA编程模型**：学习如何定义和管理CUDA内核函数，这些函数在GPU上并行执行。理解如何通过线程同步和通信机制确保正确性。 4. **数据传输**：学习如何在GPU和CPU之间高效地移动数据，包括使用cudaMemcpy函数，以及理解数据对齐和优化的重要性。 5. **内存管理**：了解如何有效地分配和管理GPU内存，包括动态内存分配和内存对齐策略。 6. **并行算法设计**：学习如何将串行算法转化为并行算法，例如使用CUDA进行矩阵乘法、归并排序等常见操作的实现。 7. **性能分析与优化**：如何使用NVIDIA的Nsight工具进行性能分析，以及如何通过优化代码结构、减少数据传输和内存访问冲突来提高GPU程序的性能。 8. **C++ AMP和OpenACC对比**：虽然CUDA是NVIDIA专有的，但也会提及与其他跨平台的GPU编程接口，如C++ AMP和OpenACC，讨论各自的优缺点和适用场景。通过学习本书，读者不仅能够掌握CUDA编程的基本技能，还能理解GPU并行计算的原理，为进一步深入研究高性能计算和人工智能领域的应用打下基础。书中实例的实践将帮助读者更好地理解理论概念，并能动手实现自己的CUDA程序。

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Preface

This book shows how, by harnessing the power of your computer’s graphics

process unit (GPU), you can write high-performance software for a wide range

of applications. Although originally designed to render computer graphics on

a monitor (and still used for this purpose), GPUs are increasingly being called

upon for equally demanding programs in science, engineering, and nance,

among other domains. We refer collectively to GPU programs that address

problems in nongraphics domains as general-purpose. Happily, although you

need to have some experience working in C or C++ to benet from this book,

you need not have any knowledge of computer graphics. None whatsoever! GPU

programming simply offers you an opportunity to build—and to build mightily—

on your existing programming skills.

To program NVIDIA GPUs to perform general-purpose computing tasks, you

will want to know what CUDA is. NVIDIA GPUs are built on what’s known as

the CUDA Architecture. You can think of the CUDA Architecture as the scheme

by which NVIDIA has built GPUs that can perform both traditional graphics-

rendering tasks and general-purpose tasks. To program CUDA GPUs, we will

be using a language known as CUDA C. As you will see very early in this book,

CUDA C is essentially C with a handful of extensions to allow programming of

massively parallel machines like NVIDIA GPUs.

We’ve geared CUDA by Example toward experienced C or C++ programmers

who have enough familiarity with C such that they are comfortable reading and

writing code in C. This book builds on your experience with C and intends to serve

as an example-driven, “quick-start” guide to using NVIDIA’s CUDA C program-

ming language. By no means do you need to have done large-scale software

architecture, to have written a C compiler or an operating system kernel, or to

know all the ins and outs of the ANSI C standards. However, we do not spend

time reviewing C syntax or common C library routines such as malloc() or

memcpy(), so we will assume that you are already reasonably familiar with these

topics.

From the Library of Daisy Alford Smith

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