triton-cuda-patterns

Implement Triton CUDA kernels by identifying the target operation pattern and applying standard templates for vector elementwise, reduction, or matmul workloads. This Skill provides code skeletons with guidance on grid/block layout and tl.dot usage for fast prototyping.

The best way to structure a Triton matmul kernel is using a standard template that leverages tl.dot for matrix multiplication. This approach provides a readable, easily modifiable implementation with proper grid and block layout guidance for ML and numerical computing.

Yes, you can use these Triton kernel patterns for custom operator development in ML inference and high-performance numerical computing. The templates support fast prototyping and educational demonstrations for vector operations, reductions, and matrix multiplication.

Prior CUDA experience helps but is not strictly required to use these Triton kernel templates. They are designed for fast prototyping and educational purposes, providing standard implementations for vector, reduction, and matmul patterns that are readable and easy to modify.

Triton handles reduction operations by providing standardized kernel templates that define grid and block layouts for vector reductions. These templates allow developers to quickly generate usable kernel skeletons for numerical computing without writing low-level boilerplate.

Use Triton instead of raw CUDA for elementwise operations when you need fast prototyping, template-based code generation, or educational demonstrations. Triton provides readable, reusable implementation patterns for vector operations that are easy to modify and compare performance-wise.