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DTSTART:20210427T070000Z
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DESCRIPTION:General description and learning outcomes\n\nWe would like to i
 nvite you to participate in our High-performance data analytics course whe
 re we will introduce different tools and methods for Big data handling. Th
 e tools will be presented on two different use cases but can be applied to
  any data.\n\nHiDALGO (https://hidalgo-project.eu/) – HPC and Big Data T
 echnologies for Global Systems – is a European project funded by the Hor
 izon 2020 Framework Programme of the European Union. The project is carrie
 d out by 13 institutions from seven countries. \n\nThis training event wi
 ll start with an introductory talk to provide a view of high-performance d
 ata analytics (HPDA) from the HiDALGO perspective. The main concepts will 
 be presented\, listing the tools that have been used\, together with infor
 mation about benchmarks the consortium has done (as a source of informatio
 n about their scalability). This introduction also presents how these tool
 s are being applied in HiDALGO\, in order to solve different problems.\n\n
 The following part of the training will focus on HPC and HPDA technologies
 \, applied to use-cases such as Urban Air Pollution (UAP). The UAP applica
 tion is a software framework for modeling the vehicular traffic emitted ai
 r pollution and its dispersion at very high resolution by using geometry i
 nputs (Open Street Map)\, coupled weather data (ECMWF) and traffic simulat
 ion (SUMO)\, computational fluid dynamics (CFD) tools running on HPC infra
 structures (OpenFOAM)\, and evaluation with HPDA methods.\n\nThis HPC/HPDA
 /UAP-part of the training will introduce the UAP concept\, workflows\, imp
 lementations\, application of the CFD-module in HPC environment\, deployme
 nt to HPC\, running\, and evaluation. Participants will learn the techniqu
 es of these parts from a general perspective\, namely\, HPC workflow model
 ing (TOSCA in YAML rendering)\, basics of OpenFOAM for computation of air 
 pollutant dispersion using HPC\, and the applied HPDA methods for fast eva
 luation.\n\nThe last part will provide an introduction to the data availab
 le at ECMWF and Copernicus\, and the APIs for retrieving the data\, follow
 ed by practical sessions on data exploration and manipulation. After this 
 web-seminar\, participants will be able to independently discover weather\
 , climate\, and environmental data produced and hosted by ECMWF\, and also
  to retrieve and process these data using Python libraries.\n\nFor whom is
  the workshop\n\nResearchers\, practitioners\, and developers who are int
 erested in implementation of HPC workflows and HPDA. Environmental scient
 ists that would like to apply microscale models and exploit the strength 
 of HPC easily.\n\nThe part provided by ECMWF is aimed in particular at res
 earchers that would like to use weather\, climate\, or environmental data 
 in their work.\n\nPrerequisites\n\n\n	Participants are expected to have so
 me basic knowledge about Big Data technologies (although not mandatory).\n
 	Participants need to have basic knowledge of Linux CLI for the developer 
 parts of the UAP training. For the application side\, basic environmental 
 knowledge related to air pollution needed (although not mandatory).\n	For 
 the part related to weather\, participants are expected to have basic Pyth
 on knowledge and be comfortable using Jupyter Notebooks. They will have th
 e option to either use the mybinder.org platform or work locally. The gith
 ub repository with the notebooks and a list of libraries will be provided 
 before the workshop.\n\n\nInstructors\n\nZoltán Horváth\, Ákos Kovács
 \, László Környei\, Mátyás Constans\, Széchenyi István Universit
 y\, Győr\nMilana Vuckovic\, European Centre for Medium-Range Weather For
 ecasts\n\nAgenda\n\nFor the updated agenda and information on the sessions
  check the event's webpage at \nhttps://enccs.se/events/2021/04/enccshida
 lgo-workshop-on-high-performance-data-analytics/\nhttps://events.prace-ri.
 eu/event/1193/
SUMMARY:[ONLINE] Workshop on High-performance Data Analytics @ENCCS/HiDALGO
URL;VALUE=URI:https://events.prace-ri.eu/event/1193/
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