CUDA权威指南:全面探索GPU编程(含CUDA 5.0与开普勒架构)

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《CUDA专家手册》(The CUDA Handbook) 是一本全面指南,专注于GPU编程,特别是使用NVIDIA的CUDA技术。该书由Nicholas Wilt撰写,涵盖了CUDA 5.0和开普勒架构的最新特性,适合那些希望深入了解GPU并行计算的开发者。CUDA是一种编程模型,允许程序员在NVIDIA GPU上编写高效的并行代码,从而加速科学计算、机器学习和图形处理等领域的任务。 本书结构清晰,共分为三个主要部分: 1. **基础知识概述**:这部分首先介绍了CUDA硬件的支持背景,包括NVIDIA GPU的架构、CUDA架构和编程环境的设置。它为读者提供了一个高屋建瓴的视角,帮助理解CUDA技术的基础原理,包括CUDA编程模型、线程和块的概念,以及如何有效地管理和调度这些并行单元。 2. **CUDA编程细节**:这是本书的核心内容,详细讲解了CUDA编程的各种技巧和最佳实践。包括但不限于CUDA编程语言C++的特性和扩展,内存管理(如全局内存、共享内存和纹理内存),同步与互斥,以及优化性能的技术。这部分还包括了对CUDA API函数的深入剖析,让开发者能掌握如何编写高效、可伸缩的GPU程序。 3. **案例剖析**:通过深入分析精选的CUDA应用场景和关键的并行算法,读者可以了解到CUDA在实际项目中的应用和效果。这部分案例涵盖了从基础的数学运算到复杂的科学计算和图形渲染,旨在帮助读者理解和掌握如何将CUDA应用于解决实际问题。 此外,值得注意的是,《CUDA专家手册》提供了大量的开源代码示例,超过25000行,供开发者参考和学习。这些代码可以直接用于实践中,大大增强了本书的实用价值。书中还强调了版权和责任声明,指出尽管作者和出版商已经尽力确保信息准确,但不承担任何明示或暗示的保修义务,并且不对因使用书中的信息或程序可能产生的附带或间接损害负责。 该书预计于2013年底由哈尔滨工业大学软件学院的苏统华教授组织翻译,并由机械工业出版社出版,为国内的CUDA开发者提供了一本权威且实用的学习资源。对于想要进入GPU编程领域或者提升CUDA技能的专业人士来说,这本书是一个不可或缺的参考资料。
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The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as well as the guidance on how best to structure CPU/GPU data interchange and synchronization. The accompanying open source code-more than 25,000 lines of it, freely available at www.cudahandbook.com-is specifically intended to be reused and repurposed by developers. Designed to be both a comprehensive reference and a practical cookbook, the text is divided into the following three parts: Part I, Overview, gives high-level descriptions of the hardware and software that make CUDA possible. Part II, Details, provides thorough descriptions of every aspect of CUDA, including * Memory * Streams and events * Models of execution, including the dynamic parallelism feature, new with CUDA 5.0 and SM 3.5 * The streaming multiprocessors, including descriptions of all features through SM 3.5 * Programming multiple GPUs * Texturing The source code accompanying Part II is presented as reusable microbenchmarks and microdemos, designed to expose specific hardware characteristics or highlight specific use cases. Part III, Select Applications, details specific families of CUDA applications and key parallel algorithms, including * Streaming workloads * Reduction * Parallel prefix sum (Scan) * N-body * Image ProcessingThese algorithms cover the full range of potential CUDA applications.