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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.

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Summary
This summary is machine-generated.

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.

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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.