# cuda

Guides the agent through CUDA C/C++ work: thread hierarchy, shared memory, streams, nvcc flags and Thrust.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 320 installs · ★ 267
- Author: mohitmishra786
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/mohitmishra786/low-level-dev-skills/tree/HEAD/skills/gpu/cuda/SKILL.md
- Updated: 2026-06-27
- HTML: https://affpapa.org/en/nevibecode/skills/cuda

## What it does

This skill gives the agent a working order for NVIDIA CUDA C/C++. It covers how to lay work out across grids, blocks and threads, how to pick block sizes, and how to estimate occupancy. The memory hierarchy gets its own section: registers, `__shared__`, global, `__constant__` and texture memory, each with its usual job.

Code samples are included: vector addition, tiled matrix multiply with shared memory, and multi-stream pipelines built on `cudaMemcpyAsync`. For builds it walks through nvcc flags — `-O3`, `-arch`, `-gencode` for a fat binary, `-G` for debugging, `--use_fast_math`, `-maxrregcount`. A troubleshooting table covers `cudaErrorIllegalAddress`, kernel launch timeouts, and CPU/GPU result mismatches caused by a race on shared or global memory.

There is a Thrust section too: sort, reduce and scan through `thrust::device_vector` instead of hand-written kernels. Async copies only overlap with kernel execution on pinned memory via `cudaMallocHost` — with pageable memory you get nothing, so check that before restructuring a pipeline. Two classic slowdowns are spelled out: warp divergence from branching on `threadIdx`, and strided uncoalesced access.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add mohitmishra786/low-level-dev-skills --skill cuda -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/cuda/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/cuda/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add mohitmishra786/low-level-dev-skills --skill cuda -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/cuda/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add mohitmishra786/low-level-dev-skills --skill cuda -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/cuda/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install mohitmishra786/low-level-dev-skills cuda
```

2. **Manually: put the files in place**
- File: `.github/skills/cuda/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add mohitmishra786/low-level-dev-skills --skill cuda -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/mohitmishra786/low-level-dev-skills --path skills/gpu/cuda
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/cuda/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add mohitmishra786/low-level-dev-skills --skill cuda -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/mohitmishra786/low-level-dev-skills/tree/HEAD/skills/gpu/cuda/SKILL.md

## When to use it
- write a CUDA kernel for matrix multiply or reduction
- pick block and grid dimensions for a given problem
- track down a slow kernel caused by uncoalesced access
- build a fat binary for several architectures with `-gencode`
- swap a hand-written kernel for Thrust sort or reduce

## Example prompts
- write a CUDA kernel that adds two vectors and launch it with 256 threads per block
- why is my matrix multiply kernel slow — check coalescing and occupancy
- rewrite this reduction with Thrust instead of a custom kernel

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f8a65aabb985f2d7.

BEGIN THIRD-PARTY FILE f8a65aabb985f2d7
````text
---
name: cuda
description: CUDA C/C++ skill for NVIDIA GPU kernel programming. Use when writing CUDA kernels, managing thread/block/grid hierarchy, optimizing memory access patterns, using streams and async copies, configuring nvcc flags, or integrating Thrust. Activates on queries about CUDA kernels, nvcc, shared memory, warp divergence, occupancy, or Thrust.
---

# CUDA

## Purpose

Guide agents through NVIDIA CUDA C/C++ development: kernel launch configuration, the memory hierarchy from registers through global memory, asynchronous execution with streams, nvcc compilation flags, Thrust library usage, and diagnosing common performance pitfalls like warp divergence and uncoalesced memory access.

## When to Use

- Writing or optimizing a CUDA kernel for matrix multiply, reduction, or stencil operations
- Choosing block/grid dimensions and estimating occupancy
- Debugging slow kernels due to memory access patterns or low occupancy
- Setting up multi-stream pipelines with async `cudaMemcpyAsync`
- Compiling with nvcc and selecting architecture flags (`-gencode`)
- Using Thrust for parallel primitives instead of hand-written kernels

## Workflow

### 1. Minimal kernel and launch

CUDA organizes work as threads grouped into blocks, blocks grouped into a grid.

```
Thread hierarchy
├── grid (1D/2D/3D)
│   └── block (1D/2D/3D, max 1024 threads)
│       └── thread (threadIdx, blockIdx, blockDim, gridDim)
```

```c
// vector_add.cu
#include <cuda_runtime.h>
#include <stdio.h>

__global__ void vector_add(const float *a, const float *b, float *c, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n)
        c[i] = a[i] + b[i];
}

int main(void) {
    const int n = 1 << 20;
    size_t bytes = n * sizeof(float);
    float *h_a, *h_b, *h_c, *d_a, *d_b, *d_c;

    cudaMalloc(&d_a, bytes);
    cudaMalloc(&d_b, bytes);
    cudaMalloc(&d_c, bytes);
    // ... host init and cudaMemcpy H2D ...

    int threads = 256;
    int blocks = (n + threads - 1) / threads;
    vector_add<<<blocks, threads>>>(d_a, d_b, d_c, n);
    cudaDeviceSynchronize();

    cudaMemcpy(h_c, d_c, bytes, cudaMemcpyDeviceToHost);
    cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
    return 0;
}
```

### 2. Memory hierarchy

| Memory | Scope | Latency | Typical use |
|--------|-------|---------|-------------|
| Registers | Per-thread | ~1 cycle | Local scalars, loop indices |
| Shared (`__shared__`) | Per-block | ~5 cycles | Tile data, halo exchange |
| Global | All threads | ~400+ cycles | Large arrays, coalesced access |
````
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First 2563 of 7713 bytes. © mohitmishra786 · MIT · https://github.com/mohitmishra786/low-level-dev-skills/tree/HEAD/skills/gpu/cuda/SKILL.md

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