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Difference between revisions of "FedCloudLOFAR"

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* Number of CPUs: At least 2 per instance.|
* Number of CPUs: At least 2 per instance.|
FCUC_Files =
FCUC_Files =
* [www.lofar.org LOFAR radio-telescope web site]
* [http://www.lofar.org LOFAR radio-telescope web site]
* [http://amiga.iaa.es AMIGA4GAS project web site]
* [http://amiga.iaa.es AMIGA4GAS project web site]
* [https://wiki.egi.eu/wiki/Fedcloud-tf:How_to_use_COMPSs COMPSs] high-level tool|  
* [https://wiki.egi.eu/wiki/Fedcloud-tf:How_to_use_COMPSs COMPSs] high-level tool|  
}}
}}

Revision as of 17:42, 23 December 2014

General Information

  • Status: Preparatory
  • Start Date: 06/10/2014
  • End Date: -
  • EGI.eu contact: Diego Scardaci / diego.scardaci@egi.eu, Enol Fernandez / enol.fernandez@egi.eu
  • External contact: José Sabater Montez / jsm@iaa.es, Daniele Lezzi / daniele.lezzi@bsc.es

Short Description

This project aims to integrate calibration, analysis and modelling pipelines of radio-astronomy data into a cloud infrastructure. It is developed jointly by users of the [www.lofar.org LOFAR] radio-telescope and members of the AMIGA4GAS project.

A cloud infrastructure like the EGI Federated Cloud provides:

  • flexibility to develop innovative processing pipelines;
  • a powerful frame for parallel processing pipelines and workflows;
  • the advantage of the elastic on-demand resource consumption.

Use Case

Development of a calibration pipeline for LOFAR data. The calibration of data from the radio-interferometer LOFAR requires a high capacity of storage and parallel processing. The software -relatively difficult to install and under heavy development- will be first installed into a custom image. Several configurations of virtual clusters will be used to run and profile different calibration pipelines. LOFAR users would be able to use, at a later stage, the best configuration and pipeline for the calibration of the data.

The community would like to use the COMPSs high-level tool to port the application on the EGI Federated Cloud.

Requirements

  • Storage: about 3 TB for the tests.
  • Memory: possibility to launch instances with as much memory as possible. The amount of available memory will limit the size and resolution of the final LOFAR images. A full resolution image of 20000x20000 pixels would require about 64 GB of memory.
  • Number of instances: At least three instances, one control node and two working nodes.
  • Number of CPUs: At least 2 per instance.

Additional Files