# ARK

A GPU-driven system framework for scalable AI applications.

[![License](https://img.shields.io/github/license/microsoft/ark.svg)](LICENSE)
[![CodeQL](https://github.com/microsoft/ark/actions/workflows/codeql.yml/badge.svg)](https://github.com/microsoft/ark/actions/workflows/codeql.yml)

See [Quick Start](docs/quickstart.md) to quickly get started.

## Overview

ARK is a deep learning framework especially designed for highly optimized performance over distributed GPUs. Specifically, ARK adopts a GPU-driven execution model, where the GPU autonomously schedule and execute both computation and communication without any CPU intervention.

ARK provides a set of APIs for users to express their distributed deep learning applications. ARK then automatically schedules a GPU-driven execution plan for the application, which generates a GPU kernel code called *loop kernel*. The loop kernel is a GPU kernel that contains a loop that iteratively executes the entire application, including both computation and communication. ARK then executes the loop kernel on the distributed GPUs.

![GPU-driven System Architecture](./docs/imgs/GPU-driven_System_Architecture.svg)

## Status & Roadmap

ARK is under active development and a part of its features will be added in a future release. The following describes key features of each version.

### ARK v0.1 (Latest Release)

* The default tile-based operator scheduler
* A simple software communication stack
* Transformer inference examples

### ARK v0.2 (TBU, Sep. 2023)

* A simple operator scheduler for debugging
* Extended communication interfaces
* Support more operators
* More inference examples

### ARK v0.3 (TBU, Nov. 2023)

* Full support for the operator profiler
* High-performance collective communication
* Support more operators
* More inference & training examples

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
trademarks or logos is subject to and must follow 
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.

## Citations

<img src="./docs/imgs/logos.svg" alt="KAIST and Microsoft Logos" style="width: 350px;"/>

ARK is a collaborative research initiative between KAIST and Microsoft Research.
If you use this project in your research, please cite our [NSDI'23 paper]:

```bibtex
@inproceedings{HwangPSQCX23,
  author    = {Changho Hwang and
               KyoungSoo Park and
               Ran Shu and
               Xinyuan Qu and
               Peng Cheng and
               Yongqiang Xiong},
  title     = {ARK: GPU-driven Code Execution for Distributed Deep Learning},
  booktitle = {20th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 23)},
  year      = {2023},
  publisher = {{USENIX} Association},
}
```

[NSDI'23 paper]: https://www.usenix.org/conference/nsdi23/presentation/hwang