Yesterday, a final webinar series of the EUXDAT project started with the webinar dedicated to The EUXDAT e-Infrastructure. In case you missed it, we have its recording so you can watch it now!
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The agenda of the webinar:
Introduction of Series EUXDAT Webinars. Karel Charvát (CoO)
Introduction to series of Webinars, purpose of Webinars and introduction of EUXDAT questionnaire
Green Deal, Destination Earth and EUXDAT. Nieto De Santos, Francisco Javier (ATOS)
This webinar will describe the main features of the EUXDAT e-Infrastructure and will analyse the main topics defined in the last initiative of the EC, focusing on the Green Deal. We will explain how EUXDAT can contribute to the EC vision and which features might be used by researchers and practitioners, in order to facilitate the implementation of the proposed ideas and concepts
The EUXDAT End user Platform. Anne Haugommard, Fabien Castel (ATOS France)
In this session we will present the EUXDAT platform features offered in the web platform. An overview of the platform components and hosting e-infrastructure architecture, the data catalogue, the development environment with notebooks to develop new value-added algorithms using data connectors and geoprocessing libraries.
The orchestrator, parallelization, monitoring, and accounting in EUXDAT. Jesus Gorroñogoitia Cruz, Paolo Marangio (ATOS Spain)
This webinar presents the architecture implemented for managing the underlying infrastructure of EUXDAT, so it is possible to use Cloud and HPC resources in the Agriculture domain easily. It will present the different components involved (orchestrator, monitoring, accounting, Service Level Agreements manager) and how they are connected. In the webinar we will explain the features provided and how they can be used. We will also explain how we parallelized one of the EUXDAT scenarios and how it is possible to run it through the described components.
Parallelizing with Python-MPI. Jose M. Montañana (HLRS)
The amount of data available for Geoprocessing applications has been increasing exponentially during the last decades, as well as the size of the problems to solve, which requires increasing the computation capabilities. In order to achieve it and reduce computation time, supporting tools have been developed to ease the distribution of tasks among multiple processing nodes. In this session, we focus on an introduction to how to use Python-MPI. It allows parallelizing existing applications for its execution on HPC and on Cloud.
Discussion and feedback analysis. Karel Charvát (CoO), Jorge Lopez (ATOS)