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triton-cuda-matmul

Generate autotuned Triton kernels for CUDA matmul workloads.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates the generation and optimization of CUDA Triton matmul kernels to maximize performance for large matrix products, enabling efficient GEMM-like workloads.

Core Features & Use Cases

  • Tensor Core aware tiling and shared memory strategies for matmul, bmm, and linear layers on NVIDIA GPUs
  • Autotune-enabled kernel generation with configurable BLOCK sizes and groupings to suit different M, N, K scales
  • Host-side integration patterns for tensor inputs, verification, and benchmarking of generated kernels

Quick Start

Invoke the Triton-CUDA MatMul workflow with your matrix dimensions to generate and autotune a Triton kernel for your MxK and KxN matrices.

Frequently Asked Questions about triton-cuda-matmul

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

FAQPage Schema
How do I optimize CUDA matmul workloads for large matrices?▼

You can optimize CUDA matmul workloads by generating highly efficient Triton kernels that apply block tiling, shared memory caching, and Tensor Core utilization across NVIDIA architectures.

What's the best way to autotune GEMM kernel configurations for different M, N, and K dimensions?▼

The best way to autotune GEMM kernels is using Triton's autotune-enabled generation with configurable BLOCK sizes and groupings tailored to suit different M, N, and K scales.

Does Triton support Tensor Core aware tiling for batched matmul and linear layers?▼

Yes, Triton supports Tensor Core aware tiling and shared memory strategies for matmul, batched matmul, and fully connected layers on NVIDIA GPUs.

How do I integrate and verify generated Triton matmul kernels on the host side?▼

You integrate and verify generated Triton matmul kernels using host-side integration patterns designed for tensor inputs, execution verification, and benchmarking.

Can I use this approach for fully connected layers on large matrices?▼

Yes, this approach supports fully connected layers on large matrices by generating optimized Triton kernels that handle GEMM-like workloads efficiently.

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