Debugging Assembly Language and GPU Kernels in Visual Studio Code

on YouTube ↗01h 01m

" summary

Demos show concrete outcomes: a Raspberry Pi project builds and tests on a self‑hosted GitHub Actions runner with cmakepresets.json driving consistent configure/build/test steps and the workflow reports 2 of 2 tests passing; VS Code opens disassembly view inline with source and steps assembly instructions; NVIDIA Nsight VS Code Edition attaches to a CUDA VM, runs cuda‑gdb, and lets focus change to a specific block/thread while inspecting GPU registers — the demos run successfully.

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" chapters (35)

1. 0:00Start
2. 0:37Debugging Assembly Language and Gpu Kernels in Visual Studio Code
3. 3:12Language Specific Extensions
4. 5:03Github Code Spaces
5. 7:53Cuda Programming
6. 8:26Disassembly View while Debugging
7. 9:06Make File Tools Extension
8. 10:59Continuous Integration
9. 11:10What Is Continuous Integration
10. 12:00Github Actions
11. 12:55Github Events
12. 15:21Set Up a Ci Workflow for Raspberry Pi Using Github Actions and Github Code Spaces
13. 17:51Github Dev
14. 24:41Configure and Debug on Raspberry Pi
15. 25:08Cmake Yaml File
16. 25:59Configure and Build on Raspberry Pi
17. 27:24Key Takeaways
18. 27:56Cmake Presets
19. 32:10To Use Cmake Presets and Visual Studio Code To Run and Debug a Program on a Raspberry Pi
20. 32:36Cmaketools
21. 32:46Remote Ssh
22. 34:26Cmakepresets Json
23. 39:19The Source Code in Line with Disassembly View
24. 39:52Run Tests with the Cma Tools Extension
25. 40:35Takeaways
26. 42:15Build and Debug Cuda Programs
27. 44:31Find all References
28. 45:13Build and Debug this Project
29. 46:46Gpu Debugging
30. 47:10Debug Configurations
31. 50:38Extensions
32. 52:40Helpful Resources
33. 54:08Questions
34. 56:43Does vs Code Have To Be Installed on the Raspberry Pi
35. 58:29Profiling the Cuda Code

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