https://github.com/MachineLearningSystem/ChronusArtifact

https://github.com/MachineLearningSystem/ChronusArtifact

Last synced: 9 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/MachineLearningSystem/ChronusArtifact
• Owner: MachineLearningSystem
• License: mit
• Fork: true (S-Lab-System-Group/ChronusArtifact)
• Created: 2022-10-26T03:22:32.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-01-07T08:31:45.000Z (almost 3 years ago)
• Last Synced: 2024-08-02T19:36:29.325Z (4 months ago)
• Homepage:
• Size: 5.63 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-AI-system - Chronus: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs SOCC'21

README

        
# ChronusArtifact

## Artifact for ACM SoCC '21

[https://dl.acm.org/doi/10.1145/3472883.3486978](https://dl.acm.org/doi/10.1145/3472883.3486978)

This repository contains the artifact for the ACM SoCC '21 paper "*Chronus: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs*". It includes following 2 parts:

+ `survey`: The detailed statstical information of user survey 

+ `code`: Python Implementation of Chronus.

## Trace We Use

Helios traces (SenseTime) download from [HeliosData](https://github.com/S-Lab-System-Group/HeliosData). 

Philly traces (Microsoft) download from [philly-traces](https://github.com/msr-fiddle/philly-traces). 

## Citation 

If you use this code or survey in your research, please cite this project.

```

@inproceedings{10.1145/3472883.3486978,

  author = {Gao, Wei and Ye, Zhisheng and Sun, Peng and Wen, Yonggang and Zhang, Tianwei},

  title = {Chronus: A Novel Deadline-Aware Scheduler for Deep Learning Training Jobs},

  year = {2021},

  publisher = {Association for Computing Machinery},

  address = {New York, NY, USA},

  url = {https://doi.org/10.1145/3472883.3486978},

  doi = {10.1145/3472883.3486978},

  booktitle = {Proceedings of the ACM Symposium on Cloud Computing},

  pages = {609–623},

  numpages = {15},

  keywords = {Deadline-aware Scheduler, Deep Learning Training, Cluster Management System, GPU Datacenter},

  location = {Seattle, WA, USA},

  series = {SoCC '21}

}

```