Wfcommons

The [WorkflowHub project](http://workflowhub.org) is a community framework for a community framework that provides a collection of tools for analyzing workflow execution traces, producing realistic synthetic workflow traces, and simulating workflow executions.

This Python package provides a collection of tools for: (i) Analyzing traces of actual workflow executions; (ii) Producing recipes structures for creating workflow recipes for workflow generation; and (iii) Generating synthetic realistic workflow traces.

The current list of [available workflow recipes](https://docs.workflowhub.org/en/latest/generating_workflows.html#workflow-recipes) include the following workflow applications:

- **1000Genome:** A high-throughput data-intensive bioinformatics workflow.
- **BLAST:** A high-throughput compute-intensive bioinformatics workflow.
- **BWA:** A high-throughput data-intensive bioinformatics workflow.
- **Cycles:** A high-throughput compute-intensive scientific workflow for agroecosystems modeling.
- **Epigenomics:** A high-throughput data-intensive bioinformatics workflow.
- **Montage:** A high-throughput compute-intensive astronomy workflow.
- **Seismology:** A high-throughput data-intensive seismology workflow.
- **SoyKB:** A high-throughput data-intensive bioinformatics workflow.

In this version, we have added three new workflow generator recipes (9, 12), added a parser for Pegasus (5, 6) and Makeflow (10) workflow systems, provided the ability to increase/reduce runtime and files size by a factor (11), fixed an issue with the Montage generator (7) and file size generation (8), and performed some enhancements.

Documentation and additional information: https://workflowhub.org

The **WorkflowHub project** is a community framework for enabling scientific workflow research and development by providing foundational tools for analyzing workflow execution traces, and generating synthetic, yet realistic, workflow traces that can be used to develop new techniques, algorithms and systems that can overcome the challenges of efficient and robust execution of ever larger workflows on increasingly complex distributed infrastructures.

This Python package provides a collection of tools for: (i) Analyzing traces of actual workflow executions; (ii) Producing recipes structures for creating workflow recipes for workflow generation; and (iii) Generating synthetic realistic workflow traces.

The current list of available workflow recipes include the following workflow applications:

- **1000Genome**: A high-throughput data-intensive bioinformatics workflow.
- **Cycles**: A high-throughput compute-intensive scientific workflow for agroecosystems modeling.
- **Epigenomics**: A high-throughput data-intensive bioinformatics workflow.
- **Montage**: A high-throughput compute-intensive astronomy workflow.
- **Seismology**: A high-throughput data-intensive seismology workflow.
- **SoyKB**: A high-throughput data-intensive bioinformatics workflow.

In this version, we have improved the documentation (1), fixed an issue with the Montage generator (2), and performed some enhancements (3, 4).

Documentation and additional information: https://workflowhub.org

The **WorkflowHub project** is a community framework for enabling scientific workflow research and education by providing foundational tools for analyzing workflow execution traces, and generating synthetic, yet realistic, workflow traces that can be used to develop new techniques, algorithms and systems that can overcome the challenges of efficient and robust execution of ever larger workflows on increasingly complex distributed infrastructures.

This Python package provides a collection of tools for: (i) Analyzing traces of actual workflow executions; (ii) Producing recipes structures for creating workflow recipes for workflow generation; and (iii) Generating synthetic realistic workflow traces.

The current list of available workflow recipes include the following workflow applications:

- **1000Genome**: A high-throughput data-intensive bioinformatics workflow.
- **Cycles**: A high-throughput compute-intensive scientific workflow for agroecosystems modeling.
- **Epigenomics**: A high-throughput data-intensive bioinformatics workflow.
- **Montage**: A high-throughput compute-intensive astronomy workflow.
- **Seismology**: A high-throughput data-intensive seismology workflow.
- **SoyKB**: A high-throughput data-intensive bioinformatics workflow.

Documentation and additional information: https://workflowhub.org