dispel4py: A Python framework for data-intensive scientific computing
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 58685865
- Type
- D - Journal article
- DOI
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10.1177/1094342016649766
- Title of journal
- International Journal of High Performance Computing Applications
- Article number
- -
- First page
- 316
- Volume
- 31
- Issue
- 4
- ISSN
- 1094-3420
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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A - Computer Systems
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This journal paper is a extended version of two papers (DISCS'14 workshop and IEEE eScience15 conference). It describes a new open-source library for creating stream-based workflows, which is the only framework that provides automatic parallelization using different parallel engines. Evaluations show that it is successful in enacting on different platforms, while providing performance. It has led to four international collaborations, further papers, and a PhD Thesis. The EU funded VERCE and EPOS projects have adopted it, as well as the Seismology and Climate scientific comunities to develop their real-time streaming applications, as it shows the EU-H2020 DARE project http://project-dare.eu/.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -