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Methodological Approach to Data-Centric Cloudific- ation of Scientific Iterative Workflows

Auteur(s)
Maison d'édition
: Springer, LNCS 10048
Date de parution
2016-12-14
De la page
469
A la page
482
Mots-clés
Résumé
The computational complexity and the constantly increas- ing amount of input data for scientific computing models is threatening their scalability. In addition, this is leading towards more data-intensive scientific computing, thus rising the need to combine techniques and in- frastructures from the HPC and big data worlds. This paper presents a methodological approach to cloudify generalist iterative scientific work- flows, with a focus on improving data locality and preserving perfor- mance. To evaluate this methodology, it was applied to an hydrologi- cal simulator, EnKF-HGS. The design was implemented using Apache Spark, and assessed in a local cluster and in Amazon Elastic Compute Cloud (EC2) against the original version to evaluate performance and scalability.
Notes
, 2016
Nom de l'événement
16th International Conference on Algorithms and Architecture for Parallel Processing, ICA3PP 2016
Lieu
Granada, Spain
URI
DOI
Autre version
http://link.springer.com/chapter/10.1007/978-3-319-49583-5_36
Type de publication
Resource Types::text::conference output::conference proceedings::conference paper