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JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing

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  • David K Brown
  • David L Penkler
  • Thommas M Musyoka
  • Özlem Tastan Bishop

Abstract

Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi) at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS.

Suggested Citation

  • David K Brown & David L Penkler & Thommas M Musyoka & Özlem Tastan Bishop, 2015. "JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0134273
    DOI: 10.1371/journal.pone.0134273
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    References listed on IDEAS

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    1. Parichit Sharma & Shrikant S Mantri, 2014. "WImpiBLAST: Web Interface for mpiBLAST to Help Biologists Perform Large-Scale Annotation Using High Performance Computing," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
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    1. Azza E Ahmed & Jacob Heldenbrand & Yan Asmann & Faisal M Fadlelmola & Daniel S Katz & Katherine Kendig & Matthew C Kendzior & Tiffany Li & Yingxue Ren & Elliott Rodriguez & Matthew R Weber & Justin M , 2019. "Managing genomic variant calling workflows with Swift/T," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-20, July.

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