CUDA C++ and PTX Kernel Development

Goal

Use the bundled CUDA, PTX, and profiling references in this skill to implement, debug, and optimize CUDA kernels without relying on agent-specific install paths or ad hoc web searches.

When to Use

Use this skill when you need to:

• write or review CUDA C++ kernels or supporting host code
• reason about PTX instructions, inline PTX, Tensor Core instructions, or memory model details
• debug CUDA Runtime API or Driver API failures
• profile or optimize a kernel with Nsight Systems, Nsight Compute, compute-sanitizer, or cuda-gdb
• investigate shared memory bank conflicts, memory coalescing, occupancy, register pressure, async copy, TMA, or cluster behavior
• compare a custom operator or kernel against a PyTorch baseline for correctness or performance

Do not use this skill for: