CITATION.cff

cff-version: 1.2.0
title: "ARK: A GPU-driven system framework for scalable AI applications"
version: 0.5.0
message: >-
  If you use this project in your research, please cite it as below.
authors:
  - given-names: Changho
    family-names: Hwang
    affiliation: Microsoft Research

repository-code: 'https://github.com/microsoft/ark'
abstract: >-
  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.
license: MIT
license-url: https://github.com/microsoft/ark/blob/main/LICENSE

preferred-citation:
  type: conference-paper
  title: "ARK: GPU-driven Code Execution for Distributed Deep Learning"
  authors:
  - given-names: Changho
    family-names: Hwang
    affiliation: Microsoft Research, KAIST
  - given-names: KyoungSoo
    family-names: Park
    affiliation: KAIST
  - given-names: Ran
    family-names: Shu
    affiliation: Microsoft Research
  - given-names: Xinyuan
    family-names: Qu
    affiliation: Microsoft Research
  - given-names: Peng
    family-names: Cheng
    affiliation: Microsoft Research
  - given-names: Yongqiang
    family-names: Xiong
    affiliation: Microsoft Research
  conference:
    name: 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI '23)
    city: Boston
    region: MA
    country: US
  month: 4
  year: 2023
  url: https://www.usenix.org/conference/nsdi23/presentation/hwang