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Methodological Approach to Data-Centric Cloudific- ation of Scientific Iterative Workflows
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.
   
Mots-clés Cloud Computing · Cloudification · Iterative workflows · Map Re- duce · Apache Spark · Hydrology · HydroGeoSphere · Ensemble Kalman filter
   
Citation P. Kropf, "Methodological Approach to Data-Centric Cloudific- ation of Scientific Iterative Workflows," in 16th International Conference on Algorithms and Architecture for Parallel Processing, ICA3PP 2016, Granada, Spain, 2016, p. 469-482.
   
Type Actes de congrès (Anglais)
Nom de la conférence 16th International Conference on Algorithms and Architecture for Parallel Processing, ICA3PP 2016 (Granada, Spain)
Date de la conférence 14-12-2016
Editeur commercial Springer, LNCS 10048
Pages 469-482
URL http://link.springer.com/chapter/10.1007/978-3-319-49583-...