pypto-case-matmul-2d

Generate tiled 2D matrix-multiply kernels with Pypto for large-scale workloads.

6|1|Updated Apr 19, 2026
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-matmul-2d
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pypto-case-matmul-2d
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/pypto/cases/pypto-case-matmul-2d
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-matmul-2d

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This module provides a pattern and kernel factory for high-performance 2D matrix multiplication using Pypto, enabling loop tiling and tail-handling to optimize large-scale matrix workloads.

Core Features & Use Cases

  • 2D matmul with loop tiling: efficient partitioning of matrix multiplication for large N*M.
  • Tail handling: correct processing of remainder rows with minimal overhead.
  • Pypto frontend integration: generates and compiles a kernel via a factory function for given dimensions.

Quick Start

Generate and run the 2D matmul kernel with loop tiling for the given matrices.

Frequently Asked Questions about pypto-case-matmul-2d

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

FAQPage Schema
How do I optimize large-scale matrix multiplication with loop tiling?▼

Large-scale matrix multiplication is optimized by partitioning the workload into chunked tile-based kernels, which efficiently process large N*M dimensions and handle remainder rows with minimal overhead.

What is loop tiling in 2D matmul kernels?▼

Loop tiling in 2D matmul kernels is a technique that divides large matrices into smaller chunks or tiles, enabling efficient partitioned computation and correct tail handling for remainder rows.

How does the Pypto kernel factory generate a 2D matmul kernel?▼

The Pypto kernel factory generates and compiles a 2D matmul kernel by stipulating compile-time loop tiling constants tailored for given matrix dimensions.

Can I use JIT compilation for tensor matrix multiply workloads?▼

Yes, JIT compilation is utilized through the Pypto frontend integration, which generates and compiles custom tile-based kernels on demand for specific matrix dimensions.

Does tail handling correctly process remainder rows in tiled matmul?▼

Yes, tail handling ensures the correct processing of remainder rows with minimal overhead after the main tiled computation completes for large matrices.