Triton-Viz: A Visualization Toolkit for programming with Triton

Welcome to Triton-Viz, a visualization and profiling toolkit designed for deep learning applications. Built with the intention of making GPU programming on Triton more intuitive.

Table of Contents

1. About
2. Getting Started
   • Prerequisites
   • Installation of Triton_Viz
3. Working with examples
   • More puzzles
4. License

About

Triton-Viz is a visualization and analysis toolkit specifically designed to complement the development and optimization of applications written in OpenAI's Triton, an open-source programming language aimed at simplifying the task of coding for accelerators such as GPUs. Triton-Viz offers a suite of features to enhance the debugging, performance analysis, and understanding of Triton code.

Given that Triton allows developers to program at a higher level while still targeting low-level accelerator devices, managing and optimizing resources like memory becomes a crucial aspect of development. Triton-Viz addresses these challenges by providing real-time visualization of tensor operations and their memory usage. The best part about this tool is that while it does focus on visualizing GPU operations, users are not required to have GPU resources to run examples on their system.

Getting Started

Prerequisites

• Python installed (preferably the latest available version), minimum supported version is 3.10.
• Triton installed. Follow the installation instructions in the linked repository.
• Note: the below commands must be run in order.

Installation of Triton-Viz

Clone the repository to your local machine:

git clone https://github.com/Deep-Learning-Profiling-Tools/triton-viz.git
cd triton-viz
uv sync # or "uv sync --extra test" if you're running tests

If you want to run tests, run uv sync --extra test instead of uv sync. Otherwise you're all set!

Optional: Enable NKI Support

If you want to exercise the Neuron Kernel Interface (NKI) interpreter or run the NKI-specific tests:

uv sync --extra nki # or "uv sync --extra nki --extra test" if also running tests

Note that you need to specify all features that you want in one statement when using uv sync, i.e. if you want both NKI and testing support, you must run uv sync --extra nki --extra test. The below statements are wrong and will remove the NKI install when installing test packages:

uv sync --extra nki
uv sync --extra test

Testing

• To run core Triton-viz tests, run pytest tests/.
• (if NKI installed) To run NKI-specific tests, run pytest tests/ -m nki.
• To run all tests (Triton + NKI), run pytest tests/ -m "".

Working with Examples

cd examples
python <file_name>.py

CPU interpreter concurrency

When running with the Triton CPU interpreter (TRITON_INTERPRET=1), you can emulate concurrent SMs by setting how many blocks execute in parallel:

export TRITON_VIZ_NUM_SMS=4  # or set triton_viz.config.num_sms in Python

This is useful for kernels that rely on cross-block synchronization (e.g., producer/consumer patterns) when testing without a GPU.

More Puzzles

If you're interested in fun puzzles to work with in Triton, do check out: Triton Puzzles

License

Triton-Viz is licensed under the MIT License. See the LICENSE for details.

Publication

If you find this repo useful for your research, please cite our paper:

@inproceedings{ramesh2025tritonviz,
  author={Ramesh, Tejas and Rush, Alexander and Liu, Xu and Yin, Binqian and Zhou, Keren and Jiao, Shuyin},
  title={Triton-Viz: Visualizing GPU Programming in AI Courses},
  booktitle = {Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS '25)},
  numpages = {7},
  location = {Pittsburgh, Pennsylvania, United States},
  series = {SIGCSE TS '25}
}

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