The Cooperative Computing Lab

We design software that enables our collaborators to easily harness large scale distributed systems such as clusters, clouds, and grids. We perform fundamental computer science research that enables new discoveries through computing in fields such as physics, chemistry, bioinformatics, biometrics, and data mining.

All of our softwares are open source and available on GitHub. We are always eager to welcome new collaborators—whether you want to explore our software, contribute to development, or receive support. Explore our tools to see how they can power your distributed computing needs, and visit the getting help section if you need assistance.

Workshop on Harmonizing Python Workflows at IEEE e-Science 2025 (Sep 16, 2025)

Open Source Software

TaskVine is our third-generation workflow system for building scalable data intensive applications that run on HPC clusters, cloud services, and other clusters.

Floability is an NSF funded research project to enable the rapid and portable deployment of notebooks expressing complex scientific workflows across a wide range of cyberinfrastructure.

Work Queue is an application framework for creating and managing dynamic manager-worker style programs that scale up to tens of thousands of machines on clusters, clouds, and grids.

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Research Projects

XGFabric : Coupling Sensor Networks and HPC Facilities with Advanced Wireless Networks for Near Real-Time Simulation of Digital Agriculture (DOE ASCR)

This project will explore the design of systems for connecting remote wireless sensor networks with high performance computing systems.

SADE: A Safety-Aware Ecosystem of Reputable sUAS

The overall goal of the project is to develop technology for safety zones that permit only trusted drones to operate in congested or sensitive airspace.

Pledge: Accelerating Data Intensive Scientific Workflows with Consistency Contracts

Expresses workflow I/O intentions using consistency contracts to enable efficient, cluster-wide storage and execution optimizations.

NBFlow: From Notebook to Workflow and Back Again (CSSI Framework)

NBFlow brings together interactive notebook technologies (Jupyter), reproducibility tools (SciUnit), and distributed workflows (TaskVine) to enable easy execution of large-scale notebook workflows on heterogeneous HPC clusters.

TaskVine: A User Level Framework for Data Intensive Scientific Applications (CSSI Element)

TaskVine is open source software for building large scale data intensive dynamic workflows that run on HPC clusters, GPU clusters, and commercial clouds.

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Community Highlights

Workshop on Harmonizing Python Workflows at IEEE e-Science 2025

We helped host the Workshop on Harmonizing Python Workflows at IEEE International Conference on e-Science on Monday, 15 Sep 2025.

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Latest News and Posts

Sep 16, 2025	Workshop on Harmonizing Python Workflows at IEEE e-Science 2025
Sep 09, 2025	Welcome Back, Colin!
Sep 03, 2025	New Semester, New Faces
May 22, 2025	CCL Team at GCASR 2025
Mar 26, 2025	a post with plotly.js

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selected publications

Liberating the Data Aware Scheduler to Achieve Locality in Layered Scientific Workflow Systems

Colin Thomas and Douglas Thain

In IEEE Conference on eScience, 2025

Bib PDF

@inproceedings{liberating-escience-2025,
  author = {Thomas, Colin and Thain, Douglas},
  title = {{Liberating the Data Aware Scheduler to Achieve Locality in Layered Scientific Workflow Systems}},
  booktitle = {{IEEE Conference on eScience}},
  pages = {9},
  year = {2025},
  cclpaperid = {1004}
}

Reshaping High Energy Physics Applications for Near-Interactive Execution Using TaskVine

Barry Sly-Delgado, Ben Tovar, Jin Zhou, and Douglas Thain

In ACM/IEEE Supercomputing, 2024

DOI Bib PDF

@inproceedings{reshaping-sc-2024,
  author = {Sly-Delgado, Barry and Tovar, Ben and Zhou, Jin and Thain, Douglas},
  title = {{Reshaping High Energy Physics Applications for Near-Interactive Execution Using TaskVine}},
  booktitle = {{ACM/IEEE Supercomputing}},
  pages = {1-11},
  year = {2024},
  cclpaperid = {996},
  keywords = {taskvine},
  doi = {10.1109/SC41406.2024.00068}
}

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