cuda的PACK

https://devtalk.nvidia.com/default/topic/387841/structure-pack-issue/

When I copy an instance of the following structurefrom Visual Studio C++ code to CUDA code I get erroneous results.

    struct DimmVars

    { 

       uint2 dimData;   

       uint2 dimSegment;   

       unsigned * pSrc;

    };

 

Some further investigation tells me that the sizes of the structure in C++is different then in CUDA. 



       printf("SizeofDimmVars: %d \r\n", sizeof(_dimmVars));

 

In Visual C++ this prints: Sizeof DimmVars: 20

In Cuda this prints: Sizeof DimmVars: 24

I thought this was a pack issue, but #pragma pack(show) tells me thatpragma size is for c++ the default (8) (cuda does not support this feature). SoI'm a little bit lost, and am not sure how to solve the problem. Can anybodyhelp?


I found the solution.

Apparantly the built-in types are defined to be aligned on a 8 byte boundary.The problem is that this is done only for the CUDA compiler.

An example type:

    /*DEVICE_BUILTIN*/

    struct __builtin_align__(8) uint2

    {  

       unsigned int x, y;

    };

The __builtin_align__ is defined for the CUDAcompiler, but not for the GCC/MSVC compiler, as a result the data is mostlikely aligned differently. So when you copy a structure containing this typefrom Visual C++ to CUDA, data can be misaligned (depending on the layout of thestructure). 

 

For me, the solution was to modify the structuredefinition to: 

    struct DimmVars  

    {  

       uint2 __align__(8) dimData;

       uint2 __align__(8) dimSegment;    

       unsigned * pSrc;

    };

My suggestion is to at least mention this in theCUDA SDK Sample for the CPP-integration project, which shows how simple it isto use built-in types in C++ code.

 

### 如何设置和配置CUDA开发环境 #### 插件安装 为了支持C++和CUDA的联合调试,在Visual Studio Code (VSCode) 中需要安装一系列必要的扩展插件。这些插件包括但不限于Remote SSH、C/C++、C/C++ Extension Pack、Nsight Visual Studio Code Edition以及vscode-cuda[^1]。 #### 配置 `CMakeLists.txt` 文件 在项目根目录下创建并编辑 `CMakeLists.txt` 文件,确保其内容能够正确识别和支持CUDA编译需求。以下是基本的 `CMakeLists.txt` 配置示例: ```cmake cmake_minimum_required(VERSION 3.14) project(Hello_Cuda) set(CMAKE_CXX_STANDARD 14) find_package(CUDA REQUIRED) cuda_add_executable(Hello main.cu) ``` 此配置指定了最低版本要求、项目名称、C++标准,并通过 `find_package(CUDA REQUIRED)` 来查找CUDA工具链的支持[^2]。 #### CUDA Toolkit 的下载与安装 访问NVIDIA开发者官网中的[CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive),选择适合操作系统的CUDA Toolkit版本进行下载和安装。完成安装后可以通过命令行验证安装是否成功,具体方法是在CMD中执行 `nvcc --version` 命令来查看当前CUDA编译器的信息[^3]。 #### 设置系统环境变量 如果遇到CUDA运行时错误或者找不到相关库的情况,可以尝试手动添加PATH系统环境变量指向Visual Studio的特定路径。例如对于VS2022版,可能需要将如下路径加入到系统的Path环境中: ```plaintext C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.33.31629\bin\Hostx64\x64 ``` 请注意实际路径应根据个人计算机上的Visual Studio安装位置调整,尤其是确认选用的是针对64位架构优化后的二进制文件所在目录[^4]。 ---
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