CUDA编译器nvcc的说明

CUDAC++编程⼿册（总论）CUDA C++编程⼿册（总论）The programming guide to the CUDA model and interface.Use CUDA C++ instead of CUDA C to clarify that CUDA C++ is a C++ language extension not a C language.General wording improvements throughput the guide.Fixed minor typos in code examples.Updated .Added reference to NVRTC in .Clarified linear memory address space size in .Clarified usage of CUDA_API_PER_THREAD_DEFAULT_STREAM when compiling with nvccin .Updated to use cudaLaunchHostFunc instead of the deprecated cudaStreamAddCallback.Clarified that 8 GPU peer limit only applies to non-NVSwitch enabled systems in .Added section .Added reference to CUDA Compatibility in .Extended list of types supported by __ldg() function in .Documented support for unsigned short int with .Added section .Added removal notice for deprecated warp vote functions on devices with compute capability 7.x or higher in .Added documentation for __nv_aligned_device_malloc() in .Added documentation of cudaLimitStackSize in CUDA Dynamic Parallelism .Added synchronization performance guideline to CUDA Dynamic Parallelism .Documented performance improvement of roundf(), round() and updated Maximum ULP Error Table for Mathematical .Updated Performance Guidelines for devices of compute capability 7.x.Clarified Shared Memory carve out description in Compute Capability 7.x .Added missing Stream CUStream Mapping toAdded remark about driver and runtime API inter-operability, highlighting cuDevicePrimaryCtxRetain() in Driver API .Updated default value of CUDA_C ACHE_M AXSIZE and removed no longer supported environment variables from Added new Unified Memory sections: , ,Added section .CUDA C++程序设计指南CUDA模型和接⼝的编程指南。

浅析CUDA编译流程与配置方法（3）浅析CUDA编译流程与配置方法（3）三、Nvcc的命令选项的分析说明Nvcc的选项命令形式大概有以下3类：boolean (flag-)选项，单值选项和列表(multivalued-)选项。

下面是使用规则举例：-o file-o=file-Idir1,dir2 -I=dir3 -I dir4,dir5每一个选项命令都有两个名字，全称和简写，例如–I就是--include-path的简称，注本文后面的选项命令说明均只列出简称，详细见参考资料1。

一般来说，全称多用于述，简称多用于实际使用。

编译选项可按用途分为以下7大类：1、指定编译阶段的选项这类选项主要用来控制编译的阶段，用以制定哪些阶段的输入文件要被编译，如-ptx、-cuda、-gpu等等。

最经常用到的是-c，用来生成object文件。

2、File and path specifications指定相关文件的路径及名称的命令选项下面这几个用于文件和路径说明命令是我们最常见：-o ：指定输出文件的位置和名称。

如-o $(ConfigurationName)\$(InputName).obj $(InputFileName)表示将$(InputFileName)作为输入文件，$(ConfigurationName)\$(InputName).obj为输出路径及文件名-include：指定预处理和编译时预先需要包含的头文件。

-l：指定链接时需要的库文件，另外这些库文件的搜索路径必须已经被命令选项'-L'指定。

-D ：指定预处理和编译时需要的宏，如-D_DEBUG -D_CONSOLE-U ：取消一个宏定义-I：指定包含文件的搜索路径，如-I"C:\CUDA\include" -I"C:\ProgramData\NVIDIA Corporation\NVIDIA GPU Computing SDK\C\common\inc" -I表示依赖的库的路径，所以如果有时候一些dll或lab文件不在默认路径下，在这里可以添加路径！或者"$(CUDA_BIN_PATH)\nvcc.exe" -ccbin "$(VCInstallDir)bin"-isystem：指定系统的包含引用文件的搜索路径-L：指定库文件的搜索路径.-odir：指定输出文件的目录，这选项也为代码生成步骤指定合适的输出目录做准备，与命令选项--generate-dependencies直接相关，如-odir "Debug"-ccbin：指定host编译器的所在路径，如-ccbin "$(VCInstallDir)bin"或者"C:\ProgramFiles\Microsoft Visual Studio 8\VC\bin"3、调整编译器和链接器行为的选项-g：产生可调试代码，这是调试模式下是必需的-G：产生可调试的设备代码-O：产生优化代码，包括O0，O1，O2，O3，用于产生不同的指令集，现在一般采用O0（为优化，好处在于使host与device的生成代码相异度最小，不容易出差），这些优化的具体内容我还未找到详细说明，基本上也就是展开一些循环，函数，优化一些访存指令。

DU-05348-001_v7.5 | September 2015Installation and Verification on Mac OS XTABLE OF CONTENTS Chapter 1. Introduction (1)1.1. System Requirements (1)1.2. About This Document (2)Chapter 2. Prerequisites (3)2.1. CUDA-capable GPU (3)2.2. Mac OS X Version (3)2.3. Xcode Version (3)2.4. Command-Line T ools (4)Chapter 3. Installation (5)3.1. Download (5)3.2. Install (5)3.3. Uninstall (6)Chapter 4. Verification (8)4.1. Driver (8)4.2. Compiler (8)4.3. Runtime (9)Chapter 5. Additional Considerations (11)CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).CUDA was developed with several design goals in mind:‣Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.‣Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existingapplications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.This guide will show you how to install and check the correct operation of the CUDA development tools.1.1. System RequirementsTo use CUDA on your system, you need to have:‣ a CUDA-capable GPU‣Mac OS X 10.9 or later‣the Clang compiler and toolchain installed using Xcode‣the NVIDIA CUDA Toolkit (available from the CUDA Download page)Introduction T able 1 Mac Operating System Support in CUDA 7.5Before installing the CUDA Toolkit, you should read the Release Notes, as they provide important details on installation and software functionality.1.2. About This DocumentThis document is intended for readers familiar with the Mac OS X environment andthe compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation.2.1. CUDA-capable GPUTo verify that your system is CUDA-capable, under the Apple menu select About This Mac, click the More Info … button, and then select Graphics/Displays under the Hardware list. There you will find the vendor name and model of your graphics card. If it is an NVIDIA card that is listed on the CUDA-supported GPUs page, your GPU is CUDA-capable.The Release Notes for the CUDA Toolkit also contain a list of supported products.2.2. Mac OS X VersionThe CUDA Development Tools require an Intel-based Mac running Mac OSX v. 10.9 or later. To check which version you have, go to the Apple menu on the desktop and select About This Mac.2.3. Xcode VersionA supported version of Xcode must be installed on your system. The list of supported Xcode versions can be found in the System Requirements section. The latest version of Xcode can be installed from the Mac App Store.Older versions of Xcode can be downloaded from the Apple Developer Download Page. Once downloaded, the Xcode.app folder should be copied to a version-specific folder within /Applications. For example, Xcode 6.2 could be copied to /Applications/ Xcode_6.2.app.Once an older version of Xcode is installed, it can be selected for use by running the following command, replacing <Xcode_install_dir> with the path that you copied that version of Xcode to:sudo xcode-select -s /Applications/<Xcode_install_dir>/Contents/DeveloperPrerequisites 2.4. Command-Line T oolsThe CUDA Toolkit requires that the native command-line tools are already installed on the system. Xcode must be installed before these command-line tools can be installed. The command-line tools can be installed by running the following command:$ xcode-select --installNote: It is recommended to re-run the above command if Xcode is upgraded, or an older version of Xcode is selected.You can verify that the toolchain is installed by running the following command:$ /usr/bin/cc --version3.1. DownloadOnce you have verified that you have a supported NVIDIA GPU, a supported version the MAC OS, and clang, you need to download the NVIDIA CUDA Toolkit.The NVIDIA CUDA Toolkit is available at no cost from the main CUDA Downloads page. The installer is available in two formats:work Installer: A minimal installer which later downloads packages required forinstallation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.2.Full Installer: An installer which contains all the components of the CUDA Toolkitand does not require any further download. This installer is useful for systemswhich lack network access.Both installers install the driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples source code, and other resources.The download can be verified by comparing the posted MD5 checksum with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.To calculate the MD5 checksum of the downloaded file, run the following:$ openssl md5 <file>3.2. InstallUse the following procedure to successfully install the CUDA driver and the CUDA toolkit. The CUDA driver and the CUDA toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver provided with the CUDA Toolkit.If the installer fails to run with the error message "The package is damaged and can't be opened. You should eject the disk image.", then check that your security preferences are set to allow apps downloaded from anywhere to run. This setting can be found under: System Preferences > Security & Privacy > GeneralChoose which packages you wish to install. The packages are:‣CUDA Driver: This will install /Library/Frameworks/CUDA.framework and the UNIX-compatibility stub /usr/local/cuda/lib/libcuda.dylib that refers to it.‣CUDA Toolkit: The CUDA Toolkit supplements the CUDA Driver with compilers and additional libraries and header files that are installed into /Developer/ NVIDIA/CUDA-7.5 by default. Symlinks are created in /usr/local/cuda/pointing to their respective files in /Developer/NVIDIA/CUDA-7.5/. Previous installations of the toolkit will be moved to /Developer/NVIDIA/CUDA-#.# to better support side-by-side installations.‣CUDA Samples (read-only): A read-only copy of the CUDA Samples is installed in /Developer/NVIDIA/CUDA-7.5/samples. Previous installations of the samples will be moved to /Developer/NVIDIA/CUDA-#.#/samples to better support side-by-side installations.A command-line interface is also available:‣--accept-eula: Signals that the user accepts the terms and conditions of the CUDA-7.5 EULA.‣--silent: No user-input will be required during the installation. Requires --accept-eula to be used.‣--install-package=<package>: Specifies a package to install. Can be used multiple times. Options are "cuda-toolkit", "cuda-samples", and "cuda-driver".‣--log-file=<path>: Specify a file to log the installation to. Default is /var/log/ cuda_installer.log.Set up the required environment variables:export PATH=/Developer/NVIDIA/CUDA-7.5/bin:$PATHexport DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-7.5/lib:$DYLD_LIBRARY_PATHIn order to modify, compile, and run the samples, the samples must also be installed with write permissions. A convenience installation script is provided: cuda-install-samples-7.5.sh. This script is installed with the cuda-samples-7-5 package.T o run CUDA applications in console mode on MacBook Pro with both an integratedGPU and a discrete GPU, use the following settings before dropping to console mode:1.Uncheck System Preferences > Energy Saver > Automatic Graphic Switch2.Drag the Computer sleep bar to Never in System Preferences > Energy Saver3.3. UninstallThe CUDA Driver, Toolkit and Samples can be uninstalled by executing the uninstall script provided with each package:T able 2 Mac Uninstall Script LocationsAll packages which share an uninstall script will be uninstalled unless the --manifest=<uninstall_manifest> flag is used. Uninstall manifest files are located in the same directory as the uninstall script, and have filenames matching .<package_name>_uninstall_manifest_do_not_delete.txt.For example, to only remove the CUDA Toolkit when both the CUDA Toolkit and CUDA Samples are installed:$ cd /Developer/NVIDIA/CUDA-7.5/bin$ sudo perl uninstall_cuda_7.5 \--manifest=.cuda_toolkit_uninstall_manifest_do_not_delete.txtBefore continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.Ensure the PATH and DYLD_LIBRARY_PATH variables are set correctly.4.1. DriverIf the CUDA Driver is installed correctly, the CUDA kernel extension (/System/ Library/Extensions/CUDA.kext) should be loaded automatically at boot time. To verify that it is loaded, use the commandkextstat | grep -i cuda4.2. CompilerThe installation of the compiler is first checked by running nvcc -V in a terminal window. The nvcc command runs the compiler driver that compiles CUDA programs. It calls the host compiler for C code and the NVIDIA PTX compiler for the CUDA code. The NVIDIA CUDA Toolkit includes CUDA sample programs in source form. To fully verify that the compiler works properly, a couple of samples should be built. After switching to the directory where the samples were installed, type:make -C 0_Simple/vectorAddmake -C 0_Simple/vectorAddDrvmake -C 1_Utilities/deviceQuerymake -C 1_Utilities/bandwidthTestThe builds should produce no error message. The resulting binaries will appear under <dir>/bin/x86_64/darwin/release. To go further and build all the CUDA samples, simply type make from the samples root directory.4.3. RuntimeAfter compilation, go to bin/x86_64/darwin/release and run deviceQuery. Ifthe CUDA software is installed and configured correctly, the output for deviceQuery should look similar to that shown in Figure 1.Figure 1 Valid Results from deviceQuery CUDA SampleNote that the parameters for your CUDA device will vary. The key lines are the first and second ones that confirm a device was found and what model it is. Also, the next-to-last line, as indicated, should show that the test passed.Running the bandwidthTest sample ensures that the system and the CUDA-capable device are able to communicate correctly. Its output is shown in Figure 2Figure 2 Valid Results from bandwidthT est CUDA SampleNote that the measurements for your CUDA-capable device description will vary from system to system. The important point is that you obtain measurements, and that the second-to-last line (in Figure 2) confirms that all necessary tests passed.Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.If you run into difficulties with the link step (such as libraries not being found), consult the Release Notes found in the doc folder in the CUDA Samples directory.To see a graphical representation of what CUDA can do, run the particles executable.Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C Programming Guide.A number of helpful development tools are included in the CUDA Toolkit to assistyou as you develop your CUDA programs, such as NVIDIA® Nsight™ Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.For technical support on programming questions, consult and participate in the Developer Forums.NoticeALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATEL Y, "MATERIALS") ARE BEING PROVIDED "AS IS." NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSL Y DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.TrademarksNVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Copyright© 2009-2015 NVIDIA Corporation. All rights reserved.。

在windows下安装cuda安装环境在windows下，目前cuda只支持在 Visual Studio 7.x 系列、Visual Studio 8以及免费的 Visual Studio C++ 2005 Express。

所以需要预先安装以上软件中的任意一种。

下面我们以Visual Studio 2005 为例演示cuda的安装。

1、cuda安装包cuda是免费使用的，各种操作系统下的cuda安装包均可以在/object/cuda_get_cn.html上免费下载。

Cuda提供3个安装包，分别是：SDK, Toolkit和Display。

SDK包括许多例子程序和函数库。

Toolkit包括cuda的基本工具。

Display包括了NV显卡的驱动程序。

Toolkit是核心。

2、安装cuda2.1 安装cuda toolkit双击NVIDIA_CUDA_toolkit_2.0_win32.exe安装，安装完成后在安装目录下出现6个文件夹，分别是：Bin ：工具程序和动态链接库Doc ：相关文档Include ： header头文件包Lib ：程序库Open64 ：基于open64的cuda compilerSrc ：部分原始代码安装过程中toolkit自动设定了3个环境变量：CUDA_BIN_PATH、CUDA_INC_PATH和CUDA_LIB_PATH分别对应工具程序库、头文件库和程序库，预设路径为当前安装文件夹下的bin、include 和lib三个文件夹。

2.2 安装CUDA SDKSDK可以根据需要选择安装(推荐安装，因为SDK中的许多例子程序和函数库非常有用)。

2.3 安装 CUDA Display对于没有安装NV显卡的计算机，不需要安装Display安装包，程序也可以在模拟模式下运行。

3、在Visual Studio中使用cudaCUDA的主要工具是nvcc，它会执行所需要的程序，将CUDA程序编译并执行。

CUDA以及NVCC编译流程CUDA是一种用于并行计算的编程平台和API模型，由NVIDIA开发。

它使开发人员能够直接在NVIDIAGPU上进行编程，以加速各种应用程序，包括科学计算和机器学习。

CUDAC是基于C语言的编程语言，用于编写能够在GPU上并行执行的代码。

而NVCC是NVIDIACUDA编译器，用于将CUDAC代码转换为在GPU上执行的机器码。

CUDA编译流程包括以下步骤：1. 预处理（Preprocessing）：在编译之前，NVCC会调用C预处理器对代码进行宏替换、头文件包含等操作。

4.GPU代码加载和执行：编译完成后，GPU驱动程序会将机器码加载到GPU内存中，并以并行的方式执行。

NVCC编译器是一个多阶段编译器，它可以根据输入代码的特征和目标硬件架构进行优化。

具体来说，NVCC可以进行以下优化：1.并行语句转换：NVCC会将CUDAC代码中的并行语句转换为与硬件架构对应的PTX指令。

2.代码融合：如果有多个并行内核函数，NVCC可以将它们合并成一个大的内核函数，以减少函数调用的开销。

3.寄存器优化：NVCC会尽量减少使用寄存器的数量，以提高并行执行的效率。

4.内存访问优化：NVCC会尝试重新排列内存访问模式，以最大程度地减少内存访问延迟。

5.固定点算术优化：如果代码中有浮点数运算，NVCC可以通过将浮点数转换为固定点数来提高性能。

6.循环展开：NVCC会尽可能展开循环，以减少循环控制的开销。

7.数据分布和负载平衡优化：NVCC会尝试将数据在并行线程之间均匀分布，以避免负载不平衡。

总之，CUDA和NVCC编译流程使开发人员能够将任务并行化并在GPU上执行。

通过对CUDAC代码的分析和优化，NVCC能够生成高效的机器码，从而实现加速计算。

NVIDIACUDA编程指南一、CUDA的基本概念1. GPU（Graphics Processing Unit）：GPU是一种专门用于图形处理的处理器，但随着计算需求的增加，GPU也被用于进行通用计算。

相比于CPU，GPU拥有更多的处理单元和更高的并行计算能力，能够在相同的时间内处理更多的数据。

2. CUDA核心（CUDA core）：CUDA核心是GPU的计算单元，每个核心可以执行一个线程的计算任务。

不同型号的GPU会包含不同数量的CUDA核心，因此也会有不同的并行计算能力。

3. 线程（Thread）：在CUDA编程中，线程是最基本的并行计算单元。

每个CUDA核心可以执行多个线程的计算任务，从而实现并行计算。

开发者可以使用CUDA编程语言控制线程的创建、销毁和同步。

4. 线程块（Thread Block）：线程块是一组线程的集合，这些线程会被分配到同一个GPU的处理器上执行。

线程块中的线程可以共享数据，并且可以通过共享内存进行通信和同步。

5. 网格（Grid）：网格是线程块的集合，由多个线程块组成。

网格提供了一种组织线程块的方式，可以更好地利用GPU的并行计算能力。

不同的线程块可以在不同的处理器上并行执行。

6.内存模型：在CUDA编程中，GPU内存被划分为全局内存、共享内存、寄存器和常量内存等几种类型。

全局内存是所有线程可访问的内存空间，而共享内存只能被同一个线程块的线程访问。

寄存器用于存储线程的局部变量，常量内存用于存储只读数据。

二、CUDA编程模型1.编程语言：CUDA编程语言是一种基于C语言的扩展，可在C/C++代码中嵌入CUDA核函数。

开发者可以使用CUDA编程语言定义并行计算任务、管理线程和内存、以及调度计算任务的执行。

2. 核函数（Kernel Function）：核函数是在GPU上执行的并行计算任务，由开发者编写并在主机端调用。

核函数会被多个线程并行执行，每个线程会处理一部分数据，从而实现高效的并行计算。

详解CUDA编程CUDA 是 NVIDIA 的 GPGPU 模型，它使⽤ C 语⾔为基础，可以直接以⼤多数⼈熟悉的 C 语⾔，写出在显⽰芯⽚上执⾏的程序，⽽不需要去学习特定的显⽰芯⽚的指令或是特殊的结构。

”编者注：NVIDIA的GeFoce 8800GTX发布后，它的通⽤计算CUDA经过⼀年多的推⼴后，现在已经在有相当多的论⽂发表，在商业应⽤软件等⽅⾯也初步出现了视频编解码、⾦融、地质勘探、科学计算等领域的产品，是时候让我们对其作更深⼀步的了解。

为了让⼤家更容易了解CUDA，我们征得Hotball的本⼈同意，发表他最近亲⾃撰写的本⽂。

这篇⽂章的特点是深⼊浅出，也包含了hotball本⼈编写⼀些简单CUDA程序的亲⾝体验，对于希望了解CUDA的读者来说是⾮常不错的⼊门⽂章，PCINLIFE对本⽂的发表没有作任何的删减，主要是把⼀些台湾的词汇转换成⼤陆的词汇以及作了若⼲"编者注"的注释。

现代的显⽰芯⽚已经具有⾼度的可程序化能⼒，由于显⽰芯⽚通常具有相当⾼的内存带宽，以及⼤量的执⾏单元，因此开始有利⽤显⽰芯⽚来帮助进⾏⼀些计算⼯作的想法，即 GPGPU。

即是的 GPGPU 模型。

NVIDIA 的新⼀代显⽰芯⽚，包括 GeForce 8 系列及更新的显⽰芯⽚都⽀持 CUDA。

NVIDIA 免费提供 CUDA 的开发⼯具（包括 Windows 版本和版本）、程序范例、⽂件等等，可以在下载。

使⽤显⽰芯⽚来进⾏运算⼯作，和使⽤ CPU 相⽐，主要有⼏个好处：1. 显⽰芯⽚通常具有更⼤的内存带宽。

例如，NVIDIA 的 GeForce 8800GTX 具有超过 50GB/s 的内存带宽，⽽⽬前⾼阶 CPU 的内存带宽则在 10GB/s 左右。

2. 显⽰芯⽚具有更⼤量的执⾏单元。

例如 GeForce 8800GTX 具有 128 个 "stream processors"，频率为 1.35GHz。

A n d D e b ug g i ngCUDA Programming on the Tegra Xavier-BY KRISTOFFER ROBIN STOKKEKeep This Under Your Pillow q Volta Tuning Guidel https:///cuda/archive/10.2/volta-tuning-guide/index.html q CUDA Programmer’s Guideq https:///cuda/archive/10.2/cuda-c-programming-guide/index.html q CUDA for Tegraq https:///cuda/archive/10.2/cuda-for-tegra-appnote/index.htmlq Tegra X1 Whitepaperq https:///blog/nvidia-jetson-agx-xavier-32-teraops-ai-robotics/ q Last but not leastq CUDA-GDBq You will need itq NVPROF –if you care about performanceq https:///cuda/archive/10.2/profiler-users-guide/index.htmlTegra Xavier vs.Tegra X1Tegra Xavier Tegra X1High Performance CPU8 x Carmel 2 MB L2 +128 kB $I, 64 kB $D 4 x Cortex-A57 2 MB L2 +48 kB $I, 32 kB $DLow Power CPU None 4 x Cortex-A53512 kB L2 +32 kB $I/$D Architecture Volta MaxwellCores512 Cores (8 Volta SM)256 Cores (2 SMM, 128 Cores / SMM) Memory bitwidth256-bit64-bitL2 cache512 kB256 kBL1 cache128 kB (shared memory + L1 RO cache)•64 kB (unique shared memory)•64 kB read-only cacheGPU Compute CapabilityCompute Capability Generation1.x Tesla2.x Fermi3.x Kepler (Tegra K1)4.x5.x Maxwell (Tegra X1)6.x Pascal (Tegra X2)7.x (7.2)Volta (Tegra Xavier)7.5Turing8.x Ampere You are here•«The functional development of GPUs»•For Maxwell:•Half-precision (16-bit) floating point•Dynamic parallelism•Kernels can launch kernels•Newest CC•Tensor Core (neural network support)•CUDA Toolkits•Compiler, doc, examples etc•nvcc–version•Already installed for you•If interested:•https:///wiki/CUDAParallelism in GPUs •Massive.•Volta SM –Volta Streaming Multiprocessor •Four Warp Schedulers(WS)•4 x 16 CUDA cores•4 x 8 LD/ST units, ~16k 32-bit registers•4 x 4 Special Function Units (SFUs)•8 Tensor CoresGroup of 32 threads •128 kB shared memory*•At every clock cycle..•Each WS selects an eligible warp..•.. and dispatches two instructions•All threads should follow, more or less, thesame execution pathVolta GPU Memories§256-bit RAM interface§512kB, GPU-global L2 cache§Shared between all Volta SMs§128 kB, read-only L1 cache/ shared memory §Shared texture/ local l1 cache§Local to the SM§SM-local L1 cache§Directly addressable through sharedmemory§Local to the SM§Registers FasterThisWay§A flexible, multi-layered cache hierarchy§Improves memory bandwidth§WS selects ready (non-stalling warps)§Highly programmableGPU Memories (Continued)§The CUDA toolkit documentation introduces the following memory spaces and naming conventions..§Global memory loads: Loads from RAM, possibly through caches§«Local memory»: Register spills, code, and other§Resides in RAM or «somewhere» in the cache hierarchy, hopefully in the right place§«Shared memory»: RW L1 cache shared in a thread block§«L1 RO cache»: Cache global, read-only memory loadsCUDA Programmer’s Perspectiveq Schedule blocks of threads (execution configuration syntax )q WS schedules eligible 32-thread groups of blocksFrom CUDA Programmer’s guide.__global__ void memcpy( uint32_t * src, uint32_t * dst) {...}void main(void){dim3 block_dim(1024, 1, 1);dim3 grid_dim(1024, 1, 1);uint32_t *src, *dst;memcpy<<<grid_dim, block_dim, 0, 0>>(src, dst);...}Shared memory per blockCuda stream (default 0)CUDA Programmer’s Perspective (Cont.)__global__ void memcpy( uint32_t * src, uint32_t * dst) {int idx;idx = threadIdx.x + ( blockIdx.x * blockDim.x );dst[idx] = src[idx];return;}q Special purpose registersq threadIdx.[x/y/z] -> block index coords q blockIdx.[x/y/z] -> grid index coords q blockDim.[x/y/z] -> grid dimension sizesq In example:q blockDim.x = 1024, blockIdx.x \in [0, 1023]q Index into contiguous memorySynchronisationq ECS kernel_symbol_name<<< gridDim, blkDim, shared, stream>>> ( __VA_ARGS__ ) q Kernel launches are always asynchronousq Executing thread immediately returnsq«Worst» sync: cudaDeviceSynchronise()q Blocks until all pending GPU activity is doneq However good for debugging / testing purposesq Streamsq Streams created with cudaStreamCreate()-> + flags!q Run kernel launches and asynchronous memory copies in streamsq Sync on streams with cudaStreamSynchronize( stream)Other API Specific Detailsq Two APISq Driver APIq Runtime API <-use this(https:///cuda/archive/10.2/cuda-runtime-api/index.html) q Other modules you should have a look atq Device managementq Error handlingq Memory management, unified addressingq CUDA samples: deviceQueryq CUDA Compiler: nvccq Source files with CUDA code(*.cu) are compiled as .cpp filesq nvcc extracts CUDA code, passes rest to native c++compilerWhen Things Aren’t Going Your Way q Cuda-gdbq Just like gdbq Main advantage: captures error conditions for youq But this doesn’t mean you can get lazyq Always check error codes and break on anything != cudaSuccessq Make a macroGPU Performance Analysisq CUPTI: GPU Hardware Performance Counters (HPCs)q Usage: nvprof –e <event counters> -m <metrics> <binary> <arguments>q Summary modes, counter collection modes....q Tells you about resource usage –time, memory, floating point performance, elapsed cycles q Takes time to profile –be patient or use ./c63enc –f 5 <-make sure to trigger ME & MCq Check HPC availability with nvprof –query-events –query-metricsq Notice there are well above 100 HPCs to choose from..q...which ones matter?q I will tell you! J J JGPU Performance Analysis (Continued) q Memory usageq L1_global_load_hit, l1_local_{store/load}_hit, l1_shared_{store/load}_transactions, shared_efficiency q Instructionsq Inst_integer, inst_bit_convert, inst_control, inst_misc, inst_fp_{16/32/64}q Causes of stallingq Memory, instruction dependencies, sync...q Otherq Elapsed_cycles_smq These are for the TK1, but should be at least similar for TX1q Don’t get confused by HPCs such as {gld/gst}_throughputCode Examples。

CUDA Libraries and CUDA FortranNVIDIA CUDA LibrariesNVIDIA CUDA Libraries—CUFFT —CUBLAS —CUSPARSE —Libm (math.h)—CURAND —NPP —Thrust —CUSPNVIDIA Libraries3rd Party LibrariesApplications CUDA C/FortranCUFFT LibraryCUFFT is a GPU based Fast Fourier Transform libraryCUFFT Library Features2357N= N1*N2N1N2N1N2T,T Cooley-TukeyCUFFT in 4 easy stepsCode example:/* Create a 2D FFT plan. *//* Use the CUFFT plan to transform the signal out of place. *//* Inverse transform the signal in place.Different pointers to input and output arrays implies out of place transformation */ /* Destroy the CUFFT plan. */CUBLAS LibraryCUBLAS FeaturesUsing CUBLASInterface to CUBLAS library is in cublas.hFunction naming conventioncublas + BLAS nameEg., cublasSGEMMError handlingCUBLAS core functions do not return errorCUBLAS provides function to retrieve last error recorded CUBLAS helper functions do return errorHelper functions:Memory allocation, data transferCalling CUBLAS from CCalling CUBLAS from FORTRANTwo interfaces:ThunkingAllows interfacing to existing applications without any changesDuring each call, the wrappers allocate GPU memory, copy source data from CPU memory space to GPUmemory space, call CUBLAS, and finally copy back the results to CPU memory space and deallocate theGPGPU memoryIntended for light testing due to call overheadNon-Thunking (default)Intended for production codeSubstitute device pointers for vector and matrix arguments in all BLAS functionsExisting applications need to be modified slightly to allocate and deallocate data structures in GPGPUmemory space (using CUBLAS_ALLOC and CUBLAS_FREE) and to copy data between GPU and CPUmemory spaces (using CUBLAS_SET_VECTOR, CUBLAS_GET_VECTOR, CUBLAS_SET_MATRIX, andCUBLAS_GET_MATRIX)SGEMM example (THUNKING) ! Define 3 single precision matrices A, B, C! Initialize A, B and C! Call SGEMM in CUBLAS library using THUNKING interface (library takes care of! memory allocation on device and data movement)! Call SGEMM in host BLAS librarySGEMM example (NON-THUNKING)! Initialize A, B and C! Copy data to GPU! Call SGEMM in CUBLAS library! Copy data from GPUGEMM PerformancecuBLAS 3.2, Tesla C2050 (Fermi), ECC on MKL10.2.3,********************Performance may vary based on OS version and motherboard configuration636775301295788039400100200300400500600700800900SGEMMCGEMMDGEMMZGEMMCUBLAS3.2MKL 4THREADSUsing CPU and GPU concurrentlyDGEMM(A,B,C) = DGEMM(A,B1,C1) U DGEMM(A,B2,C2)The idea can be extended to multi-GPU configuration and to handle huge matricesFind the optimal split, knowing the relative performances of the GPU and CPU cores on DGEMM(GPU)(CPU)Overlap DGEMM on CPU and GPU // Copy A from CPU memory to GPU memory devAstatus = cublasSetMatrix (m, k , sizeof(A[0]), A, lda, devA, m_gpu);// Copy B1 from CPU memory to GPU memory devBstatus = cublasSetMatrix (k ,n_gpu, sizeof(B[0]), B, ldb, devB, k_gpu);// Copy C1 from CPU memory to GPU memory devCstatus = cublasSetMatrix (m, n_gpu, sizeof(C[0]), C, ldc, devC, m_gpu);// Perform DGEMM(devA,devB,devC) on GPU// Control immediately return to CPUcublasDgemm('n', 'n', m, n_gpu, k, alpha, devA, m,devB, k, beta, devC, m);// Perform DGEMM(A,B2,C2) on CPUdgemm('n','n',m,n_cpu,k, alpha, A, lda,B+ldb*n_gpu, ldb, beta,C+ldc*n_gpu, ldc);// Copy devC from GPU memory to CPU memory C1status = cublasGetMatrix (m, n, sizeof(C[0]), devC, m, C, *ldc);Using CUBLAS, it is very easy to express the workflow in the diagramChanges to CUBLAS API in CUDA 4.0CUSPARSENew library for sparse basic linear algebraConversion routines for dense, COO, CSR and CSC formats Optimized sparse matrix-vector multiplication Building block for sparse linear solvers1.02.03.04.0y 1y 2y 3y 4α+ β1.0 6.04.07.03.02.05.0y 1y 2y 3y 4CUDA Libm featuresImprovements•Continuous enhancements to performance and accuracy CUDA 3.1erfinvf (single precision)accuracy5.43 ulp → 2.69 ulpperformance1.7x faster than CUDA 3.0CUDA 3.21/x (double precision)performance1.8x faster than CUDA 3.1Double-precision division, rsqrt(), erfc(), & sinh() are all >~30% faster on FermiCURAND LibraryLibrary for generating random numbersFeatures:XORWOW pseudo-random generatorSobol’ quasi-random number generatorsHost API for generating random numbers in bulkInline implementation allows use inside GPU functions/kernelsSingle-and double-precision, uniform, normal and log-normal distributionsCURAND Features/v08/i14/paperCURAND use 1.2.3.4.Example CURAND Program: Host APIExample CURAND Program: Run on CPUCURAND PerformanceCURAND 3.2, NVIDIA C2050 (Fermi), ECC onG i g a S a m p l e s / s e c o n dPerformance may vary based on OS version and motherboard configurationNVIDIA Performance Primitives (NPP)–C library of functions (primitives)–well optimized–low level API:–easy integration into existing code–algorithmic building blocks–actual operations execute on CUDA GPUs–Approximately 350 image processingfunctions–Approximately 100 signal processingfunctionsImage Processing PrimitivesThrust▪A template library for CUDA—Mimics the C++ STL▪Containers—Manage memory on host and device: thrust::host_vector<T>, thrust:device_vector<T>—Help avoid common errors▪Iterators—Know where data lives—Define ranges: d_vec.begin()▪Algorithms—Sorting, reduction, scan, etc: thrust::sort()—Algorithms act on ranges and support general types and operatorsThrust Example#include <thrust/host_vector.h>#include <thrust/device_vector.h>#include <thrust/sort.h>#include <cstdlib.h>// generate 32M random numbers on the host// transfer data to the device// sort data on the device (846M keys per sec on GeForce GTX 480)// transfer data back to hostAlgorithms—for_each, transform, gather, scatter…—reduce, inner_product, reduce_by_key…—inclusive_scan, inclusive_scan_by_key… —sort, stable_sort, sort_by_key…Thrust Algorithm Performance *Thrust4.0,NVIDIATeslaC2050(Fermi)******************Interoperability (from Thrust to C/CUDA) // allocate device vectordevice_vector int// obtain raw pointer to device vector’s memoryint raw_pointer_cast// use ptr in a CUDA C kernel// Note: ptr cannot be dereferenced on the host!Interoperability (from C/CUDA to Thrust)device_ptr// raw pointer to device memoryintvoid sizeof(int// wrap raw pointer with a device_ptrdevice_ptr int// use device_ptr in thrust algorithmsfill int// access device memory through device_ptr// free memoryThrust on Google CodeCUDA FortranPGI / NVIDIA collaborationSame CUDA programming model as CUDA-C with Fortran syntax Strongly typed –variables with device-type reside in GPUmemoryUse standard allocate, deallocateCopy between CPU and GPU with assignment statements:GPU_array = CPU_arrayKernel loop directives (CUF Kernels) to parallelize loops with device dataCUDA Fortran example use cudafordevicedim3! Use standard allocate for CPU and GPU arrays ! Move data with simple assignment! Call CUDA kernelblock_size=dim3(16,16,1)<<<grid_size, block_size>>>CUDA Fortran example attributes(global)value! Indices start from 1i = threadIdx%x+ (blockIdx%x -1) * blockDim%x j = threadIdx%y+ (blockIdx%y -1) * blockDim%y If ( i <= n .and. j<=m)end ifComputing πwith CUDA Fortranπ = 4 * (∑ red points)/(∑ points)Simple example:–Generate random numbers ( CURAND)–Compute sum using of kernel loop directive–Compute sum using two stages reduction with Cuda Fortran kernels –Compute sum using single stage reduction with Cuda Fortran kernel –AccuracyCUDA Libraries from CUDA Fortran!curandGenerateUniform(curandGenerator_t generator, float *outputPtr, size_t num);!pgi$ ignore_tr odata!curandGenerateUniformDouble(curandGenerator_t generator, double *outputPtr, size_t num);!pgi$ ignore_tr odataComputing with CUF kernel! Compute pi using a Monte Carlo methoduse cudafor ! CUDA Fortran runtimeuse curand! CURAND interfacepinneddevice! Define how many numbers we want to generate! Allocate arrays on CPU and GPU! Create pseudonumber generator! Set seed! Generate N floats or double on device ! Copy the data back to CPU to check result later ! Perform the test on GPU using CUF kernel!$cuf kernel do <<<*,*>>>! Perform the test on CPU! Check the results! Print the value of pi and the error! Deallocate data on CPU and GPU! Destroy the generatorComputingMismatch between CPU/GPU 78534862 78534859GPU accuracyFERMI GPUs are IEEE-754 compliant, both for single and double precision Support for Fused Multiply-Add instruction ( IEEE 754-2008)Results with FMA could be different*from results without FMAIn CUDA Fortran is possible to toggle FMA on/off with a compiler switch: -Mcuda=nofmaExtremely useful to compare results to “golden” CPU outputFMA is being supported in future CPUsCompute pi in single precision (seed=1234567 FMA disabled)Samples= 10000 Pi=3.16720009 Error= 0.2561E-01Samples= 100000 Pi=3.13919997 Error= 0.2393E-02Samples= 1000000 Pi=3.14109206 Error= 0.5007E-03Samples= 10000000 Pi=3.14106607 Error= 0.5267E-03Samples= 100000000 Pi=3.14139462 Error= 0.1981E-03*GPU results with FMA are identical to CPU if operations are done in double precisionReductions on GPU475911142531704163475911142531704163475911142531704163475911142531704163475911142531704163475911142531704163475911142531704163475911142531704163475911142531704163Level 0:8 blocks Level 1:1 block475911142531704163Parallel Reduction: Sequential Addressing1018-10-235-2-32701102123456788-21060937-2-3270110212348713130937-2-3270110212212013130937-2-327011021412013130937-2-32701102attributes(global)shared! Check if the point is inside the circle and increment local counter! Each block writes back its partial sum ! Local reduction per block! Compute the partial sums with 256 blocks of 512 threads! Compute the final sum with 1 block of 256 threadsattributes(globalshared! load partial sums in shared memory! First thread has the total sum, writes it back to global memory。

cuda c++ 编译cmakelist 概述及解释说明1. 引言1.1 概述本文将介绍CUDA C++编译CMakeList的概述及解释说明。

随着计算机科学的快速发展，图形处理单元（GPU）已经成为并行计算领域的重要组成部分。

CUDA C++作为一种用于并行计算的编程语言，能够有效利用GPU的强大计算能力，从而提高程序的性能。

本文将重点讨论如何使用CMakeList来编译CUDA C++代码。

CMake是一个跨平台的自动化构建工具，它可以根据不同平台和编译器生成相应的构建脚本，并简化项目配置和管理过程。

通过使用CMakeList，我们可以轻松地管理项目中的源文件、头文件路径以及CUDA编译器选项和链接库依赖关系，从而方便地进行CUDA C++代码编译。

1.2 文章结构本文共分为四个部分：引言、CUDA C++ 编译CMakeList概述、CUDA C++ 编译CMakeList正文和结论。

在引言中，我们将对文章进行概述并介绍其结构。

接下来，在第二部分中，我们将详细探讨什么是CUDA C++编译以及CMakeList 简介，并对CUDA编译器选项进行解释说明。

然后，在第三部分中，我们将重点介绍如何设置项目和版本号信息、添加源文件和头文件路径以及配置CUDA 编译选项和链接库依赖关系。

最后，在结论部分，我们将总结主要要点，并对CUDA C++编译CMakeList进行展望和提出建议。

1.3 目的本文的目的是提供一个全面的指南，帮助读者了解和掌握使用CMakeList来编译CUDA C++代码的过程。

通过学习本文，读者将能够清晰地理解如何设置项目和版本号信息、添加源文件和头文件路径以及配置CUDA编译选项和链接库依赖关系。

此外，本文还将提供一些展望和建议，帮助读者进一步优化他们的CUDA C++编译CMakeList流程，并改善代码性能。

无论是初学者还是有一定经验的开发人员，都可以从本文中获得实用而宝贵的知识。