这里其实指的是 NVIDIA Nsight Visual Studio Code Edition 这个插件,我理解这个插件可以认为是CUDA-GDB的可视化界面版本,封装了命令行操作到vscode的插件的界面操作。 实验手册 参考官方文档 Getting Started with the CUDA Debugger :: NVIDIA Nsight VSCE Documentation 实验环境 Ubuntu-22.04(为了避免麻烦,可以用root...
3. VS Code扩展选择 选择合适的CUDA插件对于增强VS Code的CUDA开发能力很重要。有些插件可能不再维护,或者与最新版本的VS Code不兼容,因此选择活跃且评价良好的插件。 4. launch.json配置正确 `launch.json`中的`miDebuggerPath`应指向`nvcc.exe`,但实际上是用于启动GDB的路径。这是因为VS Code通过GDB来调试CUDA...
必要信息检测成功后,打开CUDA应用的调试窗口,获取到运行和调试区、源码区和调试按钮区三个区域,运行和调试区显示CUDA调试信息。同时VS Code面板中会显示KERNEL THREADS和INFO CUDA信息,可点开对应页面查看 ,如图4所示。 图4调试窗口 在源码区域进行调试时,当前调试的代码行会高亮显示,在代码行号处单击,会打上断点标记...
"program": "${workspaceFolder}/matrixMul", # 程序所在路径 "debuggerPath":"/usr/local/cuda-11.6/bin/cuda-gdb", # 调试器所在的路径 "preLaunchTask": "ReBuild" # 程序所在路径 } ] } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 4). 编写 tasks.json(用于build) # ...
"debuggerPath":"/usr/local/cuda-11.6/bin/cuda-gdb", # 调试器所在的路径 "preLaunchTask": "ReBuild" # 程序所在路径 } ] } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 4). 编写 tasks.json(用于build) # 共编写了两个task,分别是build 和 rebuild ...
"debuggerPath":"/usr/local/cuda-11.6/bin/cuda-gdb", # 调试器所在的路径 "preLaunchTask": "ReBuild" # 程序所在路径 } ] } 4). 编写tasks.json(用于build) # 共编写了两个task,分别是build 和 rebuild { "version": "2.0.0", "tasks": [ ...
1.3. Build the Sample and Launch the Debugger In order to build our application, we must first create integrate our build system with a task. Go to the Command Palette and execute the Tasks: Configure Default Build Task command. Here is the task configuration that is generated: { "version...
I use VS2017 to manage CMake projects. However, CUDA debugger cannot be used. I can’t click “Start CUDA Debugging” in the menu. CMakeSettings.txt { "name": "x64-Debug", "generator": "Ninja",
API Error Reporting Checking the error code of all the CUDA driver API and CUDA runtime API function calls is vital to ensure the correctness of a CUDA application. Now the debugger is able to report, and even stop, when any API call returns an error. See set cuda api_failures for ...
of the graph makes it easier for you to identify obvious configuration issues and enables you to create easy-to-understand bug reports that can be used by others to analyze and debug the issue. Combining this API with a debugger increases the usefulness, by isolating issues to specific nodes....