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Taskblaster: a generic framework for automated computational workflows
Ask Hjorth Larsen1, Mikael J Kuisma1, Tara M Boland1
1CAMD, Computational Atomic-Scale Materials Design, Department of Physics, Technical University of Denmark Kgs. Lyngby 2800 Denmark asklarsen@gmail.com.
Digital Discovery
|August 14, 2025
Summary
Taskblaster is a Python framework for managing computational workflows with automated error handling. It supports dynamic, modular designs and simplifies data maintenance for evolving projects.
Area of Science:
- Computational Science and Engineering
- Software Engineering for Scientific Research
Background:
- Managing complex computational workflows in scientific research presents challenges in modularity and data maintenance.
- Existing workflow systems may lack flexibility for dynamic task execution and robust error handling.
Purpose of the Study:
- Introduce Taskblaster, a generic and lightweight Python framework for composing, executing, and managing computational workflows.
- Enable dynamic workflow execution with features like branching and iteration, promoting modular design and simplifying data management.
- Provide a flexible system for controlling task execution with automated error handling.
Main Methods:
- Developed Taskblaster as a Python framework with a focus on modularity and reusability.
- Implemented dynamic workflow capabilities, including flow control (branches, iteration), making the system Turing complete.
- Designed a storage model using nested directories for intuitive task naming and developed command-line tools for execution control.
- Supported task execution via worker processes, adaptable to terminal execution or queueing systems for resource control.
Main Results:
- Taskblaster facilitates the creation of modular and reusable computational workflows.
- The framework supports dynamic workflows with automated error handling and flexible task execution.
- A library of workflows for materials simulations (ASR-lib) using Atomic Simulation Environment and GPAW is provided as an example.
- Taskblaster demonstrates versatility, applicable to various computational codes beyond materials simulations.
Conclusions:
- Taskblaster offers a generic, lightweight, and powerful solution for computational workflow management.
- The framework enhances modularity, simplifies data maintenance, and supports complex, dynamic computational tasks.
- Its design promotes efficient execution and resource control, benefiting scientific research and development.
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