skills.rest
xchang1121
xchang1121COMMUNITY
Explore all skills by this author →
GitHub

cuda-c-basics

Explain CUDA-C kernels, memory hierarchy, and grid/block configuration to learners.

6|1|Updated Apr 19, 2026
Search Tags:#memory#kernel#tutorial#cuda#cuda-c#gpu-programming
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill cuda-c-basics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuda-c-basics
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/cuda-c/guides/cuda-c-basics
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill cuda-c-basics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CUDA-C basics help newcomers and engineers quickly grasp core GPU programming concepts without wading through scattered references.

Core Features & Use Cases

  • Core concepts: kernel, grid, block, threads, and memory types (global, shared, registers)
  • Standard patterns: five-step kernel structure, indexing, and memory access
  • Practical guidance: snippets on kernel launch, boundary checks, and safe PyTorch integration
  • Use case: build a small CUDA kernel for vector add or simple matrix operation to learn the workflow

Quick Start

Follow this guide to set up a minimal CUDA-C kernel project and implement a simple example.

Frequently Asked Questions about cuda-c-basics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What are the core concepts needed to start CUDA-C GPU programming?▼

CUDA-C GPU programming requires understanding kernels, grids, blocks, threads, and memory types including global, shared, and registers to execute parallel workloads.

How do I configure grid and block dimensions when launching a CUDA kernel?▼

To launch a CUDA kernel, configure grid and block dimensions, implement global indexing for thread mapping, and apply boundary checks to ensure safe memory access.

Can I integrate custom CUDA kernels safely with PyTorch?▼

Yes, you can integrate custom CUDA kernels with PyTorch by following safety-oriented best practices and standard kernel launch patterns to ensure stable execution.

What is the standard five-step structure for writing a CUDA-C kernel?▼

The standard five-step CUDA-C kernel structure covers grid and block configuration, global indexing, memory access patterns, kernel launch, and boundary checks for safe execution.

Is CUDA-C suitable for engineers with no prior GPU programming experience?▼

Yes, CUDA-C is designed for students and engineers starting GPU programming, offering practical examples like vector addition to quickly grasp core concepts without scattered references.

Related Skills

Explore More Software Engineering Skills→
NVIDIA-TAONVIDIA-TAO
83

tao-run-on-docker

Run Docker containers on GPU hosts with NVIDIA Container Toolkit.

Official
Intermediate
mindspore-aimindspore-ai
258

tilelang-cuda-examples-torch

Run TileLang CUDA Torch examples for GEMM, GEMV, and LayerNorm kernels.

Official
Intermediate
tranceetrancee

kotlin-concurrency-expert

Review and remediate Kotlin Coroutines usage in Android applications.

Community
Advanced
mindspore-aimindspore-ai
258

triton-cuda-memory

Optimize GPU memory access patterns for Triton CUDA kernels.

Official
Advanced