@inproceedings{3c4c68b65af64c91971c5851cd7b5102,
title = "Dispel4py: A python framework for data-intensive eScience",
abstract = "We present dispel4py, a novel data intensive and high performance computing middleware provided as a standard Python library for describing stream-based workows. It allows its users to develop their scientific applications locally and then run them on a wide range of HPC-infrastructures without any changes to the code. Moreover, it provides automated and efficient parallel mappings toMPI, multiprocessing, Storm and Spark frameworks, commonly used in big data applications. It builds on the wide availability of Python in many environments and only requires familiarity with basic Python syntax. We will show the dispel4py advantages by walking through an example. We will conclude demonstrating how dispel4py can be employed as an easy-to-use tool for designing scientific applications using real-world scenarios.",
author = "Amrey Krause and Rosa Filgueira and Malcolm Atkinson",
note = "Funding Information: This research was supported by the VERCE project (EU FP7 RI 283543) and the Terracorrelator project (funded by NERC NE/L012979/1). Publisher Copyright: {\textcopyright} 2015 ACM.; 5th Workshop on Python for High-Performance and Scientific Computing, PyHPC 2015 ; Conference date: 15-11-2015",
year = "2015",
month = nov,
day = "15",
doi = "10.1145/2835857.2835863",
language = "English",
series = "Proceedings of PyHPC 2015: 5th Workshop on Python for High-Performance and Scientific Computing - Held in conjunction with SC 2015: The International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "ACM",
booktitle = "Proceedings of PyHPC 2015",
address = "United States",
}