CUDA C++编程指南（v11.1）：CUDA 8.x与8.6支持详解
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《CUDA C++编程指南》是NVIDIA开发者设计指南的一份重要文档，版本为PG-02829-001_v11.1，发布日期为2020年10月。这份文档主要针对CUDA C++编程进行了详尽的阐述，旨在帮助开发者充分利用NVIDIA GPU的强大计算能力进行并行计算。
该文档的重要更新包括对Compute Capability 8.x的支持，这是针对特定GPU架构的优化，提供了针对这些设备的专门指令集和功能。在版本11.1中，对于Compute Capability 8.6的介绍有所增强，涉及了算术指令的更新和特性及技术规格的概述，确保了程序的高效运行和兼容性。
章节1"Introduction"（入门）介绍了使用GPU的优势，如处理大量数据、并行计算性能提升等，以及CUDA作为一个通用目的的并行计算平台和编程模型。它强调了CUDA编程模型的可扩展性，使得开发者能够轻松地将计算任务分解到GPU的多核心上。
第2章"Programming Model"（编程模型）深入讲解了CUDA编程的核心概念，如kernel（线程块）、线程层级结构、内存层次划分，以及如何实现异构编程，即利用CPU和GPU的协同工作。此外，对Compute Capability的讨论揭示了针对不同GPU硬件特性的编程策略。
第3章"Programming Interface"（编程接口）详细说明了如何使用NVCC编译器进行CUDA C++代码的编译。这部分内容涵盖了离线编译与实时（Just-in-Time, JIT）编译的区别，以及编译后的二进制兼容性、PTX（Parallel Thread Execution）兼容性，以及对C++语言特性的支持，特别是64位兼容性，确保代码能在多种架构上无缝运行。
《CUDA C++编程指南》是一份不可或缺的参考资料，为开发者提供了CUDA C++编程的全面指南，帮助他们编写出高效、兼容且可扩展的GPU加速代码。随着GPU技术的不断发展，理解并掌握这份文档对于现代GPU应用开发至关重要。
CUDA C++ Programming Guide PG-02829-001_v11.1|xvi
I.7.1.Architecture..................................................................................................................... 326
I.7.2.Global Memory.................................................................................................................327
I.7.3.Shared Memory............................................................................................................... 327
AppendixJ.Driver API......................................................................................................328
J.1.Context................................................................................................................................... 330
J.2.Module....................................................................................................................................331
J.3.Kernel Execution................................................................................................................... 332
J.4.Interoperability between Runtime and Driver APIs.............................................................334
AppendixK.CUDA Environment Variables...................................................................... 335
AppendixL.Unified Memory Programming.................................................................... 338
L.1.Unified Memory Introduction................................................................................................338
L.1.1.System Requirements....................................................................................................339
L.1.2.Simplifying GPU Programming..................................................................................... 339
L.1.3.Data Migration and Coherency......................................................................................340
L.1.4.GPU Memory Oversubscription..................................................................................... 341
L.1.5.Multi-GPU....................................................................................................................... 341
L.1.6.System Allocator............................................................................................................ 342
L.1.7.Hardware Coherency......................................................................................................342
L.1.8.Access Counters.............................................................................................................343
L.2.Programming Model.............................................................................................................344
L.2.1.Managed Memory Opt In............................................................................................... 344
L.2.1.1.Explicit Allocation Using cudaMallocManaged().................................................... 344
L.2.1.2.Global-Scope Managed Variables Using __managed__........................................ 345
L.2.2.Coherency and Concurrency......................................................................................... 345
L.2.2.1.GPU Exclusive Access To Managed Memory......................................................... 346
L.2.2.2.Explicit Synchronization and Logical GPU Activity.................................................347
L.2.2.3.Managing Data Visibility and Concurrent CPU + GPU Access with Streams........ 348
L.2.2.4.Stream Association Examples................................................................................ 349
L.2.2.5.Stream Attach With Multithreaded Host Programs...............................................349
L.2.2.6.Advanced Topic: Modular Programs and Data Access Constraints......................350
L.2.2.7.Memcpy()/Memset() Behavior With Managed Memory.......................................... 351
L.2.3.Language Integration..................................................................................................... 352
L.2.3.1.Host Program Errors with __managed__ Variables..............................................352
L.2.4.Querying Unified Memory Support................................................................................353
L.2.4.1.Device Properties.....................................................................................................353
L.2.4.2.Pointer Attributes.................................................................................................... 353
L.2.5.Advanced Topics............................................................................................................. 353
L.2.5.1.Managed Memory with Multi-GPU Programs on pre-6.x Architectures...............353
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