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cutile-python

Generate validated cuTile GPU kernels with float16 inputs and float32 accumulators.

Updated May 23, 2026
Search Tags:#cuda#performance-optimization#kernel-validation#gpu-kernels#cutile#tile-based-programming#multi-kernel-orchestration
One-click install
npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill cutile-python-yo-steven
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cutile-python
Source: https://github.com/yo-steven/skills-exploration-20260522/tree/main/skills/TileGym/cutile-python
Command: npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill cutile-python-yo-steven

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you write correct, fast GPU kernels using cuTile’s tile-based Python DSL while avoiding common pitfalls in indexing, tile sizing, typing, and numerical validation.

Core Features & Use Cases

  • cuTile kernel authoring: Implement from scratch or adapt patterns for matmul-like tiling, data movement, and tile register workflows.
  • Debugging & correctness validation: Follow a mandatory validation loop with compile/run checks and reference comparisons to catch logic or numerical issues early.
  • Performance-oriented optimization: Apply tile- and grid-driven design choices (e.g., tile sizes as powers of two, float16 inputs with float32 accumulators) for throughput and stability.
  • Orchestrated multi-kernel workflows: Use deep agent orchestration for decompositions involving 3+ operations, multi-layer modules, or inter-kernel dependencies.

Quick Start

Invoke cutile-python to generate a validated cuTile GPU kernel for your requested operation and target tensor shapes, using float16 inputs and float32 accumulation.

Frequently Asked Questions about cutile-python

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

FAQPage Schema
How do I write high-performance GPU kernels using tile-based programming?▼

The cuTile tile-based programming model enforces an execution-backed validation loop that compiles, runs, and compares kernels against reference outputs to catch logic and numerical issues early during GPU kernel authoring.

How do I validate and debug CUDA kernel correctness during development?▼

To validate and debug CUDA kernel correctness, apply a mandatory validation loop with compile and run checks alongside reference comparisons to catch logic or numerical issues early in the development cycle.

Does cuTile support multi-kernel orchestration for multi-layer models?▼

cuTile supports multi-kernel orchestration for multi-layer models by using deep agent orchestration to decompose operations involving 3 or more steps and managing inter-kernel dependencies effectively.

What are the constraints for tile dimensions and data types in cuTile GPU kernels?▼

cuTile GPU kernels require power-of-two tile dimensions, tile-indexed ct.load and ct.store operations, typed constants, and float16 inputs paired with float32 accumulators to ensure throughput and numerical stability.

When should I use a tile-based programming model instead of standard CUDA for performance optimization?▼

You should use a tile-based programming model for performance optimization when authoring matmul-like tiling operations, data movement, and tile register workflows that require strict correctness-first validation for cuDNN or CUDA-style workloads.

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