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cuda-c-examples-torch

Integrates CUDA C kernels into PyTorch via load_inline for GPU ops.

6|1|Updated Apr 19, 2026
Search Tags:#gpu#high-performance#pytorch#cuda#inline-kernel#cpp-extension
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill cuda-c-examples-torch
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Please help me install this Agent Skill.
Skill: cuda-c-examples-torch
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/cuda-c/guides/cuda-c-examples-torch
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill cuda-c-examples-torch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill demonstrates how to integrate CUDA C kernels into PyTorch projects by using Torch's load_inline to compile and bind custom kernels at runtime, enabling high-performance GPU ops alongside Python code.

Core Features & Use Cases

  • Inline CUDA/C++ kernel integration with PyTorch via load_inline.
  • Ready-to-run examples: vector_add, relu, matmul, softmax, layernorm, and a fused kernel workflow.
  • Use cases include accelerating custom ops in research experiments or production-grade training loops requiring tailored GPU kernels.

Quick Start

Run a sample PyTorch script that loads an inline CUDA C kernel using load_inline and executes a simple operation.

Frequently Asked Questions about cuda-c-examples-torch

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

FAQPage Schema
How do I integrate custom CUDA C kernels into a PyTorch project?▼

You can integrate CUDA C kernels into PyTorch by using Torch's load_inline to compile and bind custom GPU ops at runtime, enabling high-performance execution directly alongside your Python code.

What GPU operations can I implement using load_inline with CUDA?▼

Using load_inline with CUDA, you can implement and run a suite of GPU operations including vector_add, relu, matmul, softmax, layernorm, and fused kernel variants.

Do I need a specific CUDA toolchain to compile PyTorch inline kernels?▼

Yes, compiling PyTorch inline kernels requires PyTorch with CUDA support and a properly configured CUDA toolchain to successfully compile and run the custom GPU operations.

When should I write custom CUDA kernels instead of using built-in PyTorch operators?▼

You should write custom CUDA kernels when accelerating tailored GPU operations in research experiments or production-grade training loops that require high-performance execution beyond standard PyTorch operators.

Can I create fused kernel workflows using PyTorch load_inline?▼

Yes, you can create fused kernel workflows using PyTorch load_inline, which allows you to compile and bind fused CUDA C kernel variants for immediate high-performance use in Python.

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