dispel4py: A Python Framework for Data-Intensive Scientific Computing

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.