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tilelang-cuda-patterns

Apply TileLang CUDA kernel patterns for elementwise, reduction, matmul, and gemv operations.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It provides ready-to-use templates and guidance to implement TileLang CUDA kernels, speeding up the development of common programming modes.

Core Features & Use Cases

  • Standardized elementwise, reduction, matmul, and gemv patterns with clear code structure templates.
  • Quick-start scaffolds to jump-start kernel development and educational examples for CUDA pattern recognition.
  • Use Cases: build fast prototypes for image processing, ML kernels, or numerical routines using TileLang CUDA.

Quick Start

Run the TileLang CUDA patterns guide to scaffold a kernel for a simple elementwise operation.

Frequently Asked Questions about tilelang-cuda-patterns

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

FAQPage Schema
How do I implement standard CUDA kernel patterns using TileLang?▼

TileLang CUDA kernel patterns provide ready-to-use templates to implement standard programming modes. You can quickly scaffold code for elementwise, reduction, matmul, and gemv operations to accelerate kernel development.

What is the best way to write a GEMV or reduction kernel in TileLang?▼

The best way to write a GEMV or reduction kernel in TileLang is using standardized pattern templates. These templates guide the code structure for common programming modes, speeding up development for numerical routines and ML kernels.

Can I use TileLang CUDA patterns for machine learning and image processing kernels?▼

Yes, you can use TileLang CUDA patterns for machine learning and image processing kernels. The skill provides quick-start scaffolds to build fast prototypes for numerical routines across standard elementwise and matmul operations.

What TileLang templates are available for elementwise and matmul operations?▼

Available TileLang templates cover elementwise, reduction, matmul, and gemv patterns. These templates offer clear code structure and scaffolds to jump-start CUDA kernel development for standard programming modes.

Do I need any specific dependencies to start building TileLang CUDA kernels?▼

No specific dependencies are required to start building TileLang CUDA kernels. The skill provides self-contained code scaffolds and educational examples for CUDA pattern recognition to quickly implement standard programming modes.

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