This repository provides interactive puzzles designed to teach the Triton programming language, targeting developers and researchers aiming to optimize AI workloads on accelerators like GPUs. It offers a hands-on approach to understanding Triton's low-level details, particularly memory management, through a series of progressively challenging exercises.

How It Works

The puzzles leverage a Triton interpreter, allowing users to learn and experiment without requiring direct GPU access. This approach simplifies the learning curve by abstracting away hardware dependencies, enabling users to focus on Triton's syntax, semantics, and memory access patterns, which are crucial for performance optimization.

Quick Start & Requirements

• Install via pip: pip install triton-puzzles
• Requires Python 3.8+
• No GPU required for puzzles.
• Official documentation: https://github.com/srush/Triton-Puzzles

Highlighted Details

• Interactive learning environment for Triton.
• Covers fundamental concepts to advanced algorithms like Flash Attention.
• No GPU hardware needed due to interpreter usage.
• Part of a larger series of GPU programming puzzles.

Maintenance & Community

• Active community via Discord: https://discord.gg/gpumode (#triton-puzzles channel).
• Developed by Sarah Bird (srush), with contributions from Tejas Ramesh and Keren Zhou.

Licensing & Compatibility

• License: MIT.
• Compatible with commercial and closed-source projects.

Limitations & Caveats

The puzzles are designed for learning Triton's core concepts and memory operations; they do not involve actual GPU execution, which may be a limitation for users seeking to test performance on hardware.