Dispel4py: A python framework for data-intensive scientific computing

Rosa Filguiera, Iraklis Klampanos, Amrey Krause, Mario David, Alexander Moreno, Malcolm Atkinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (SciVal)


This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. The main aim of dispel4py is to enable scientists to focus on their computation instead of being distracted by details of the computing infrastructure they use. Therefore, special care has been taken to provide dispel4py with the ability to map abstract workflows to different enactment platforms dynamically, at run time. In this work we present four dispel4py mappings: Apache Storm, MPI, multi-threading and sequential. The results show that dispel4py is successful in enacting on different platforms, while also providing scalable performance.

Original languageEnglish
Title of host publication2014 International Workshop on Data Intensive Scalable Computing Systems
Number of pages8
ISBN (Electronic)9781479970384
Publication statusPublished - 6 Apr 2014
Event2014 International Workshop on Data-Intensive Scalable Computing Systems - New Orleans, United States
Duration: 16 Nov 2014 → …


Conference2014 International Workshop on Data-Intensive Scalable Computing Systems
Abbreviated titleDISCS 2014
Country/TerritoryUnited States
CityNew Orleans
Period16/11/14 → …


  • data streaming
  • Data-intensive computing
  • e-Infrastructures
  • programming frameworks
  • Python
  • scientific workflows

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Networks and Communications


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