Issue |
EPJ Web of Conferences
Volume 116, 2016
Very Large Volume Neutrino Telescope (VLVnT-2015)
|
|
---|---|---|
Article Number | 07002 | |
Number of page(s) | 4 | |
Section | Computing Models, Data Repositories, Virtual Observatory, Data Formats, Software Systems, User Training | |
DOI | https://doi.org/10.1051/epjconf/201611607002 | |
Published online | 11 April 2016 |
https://doi.org/10.1051/epjconf/201611607002
Enabling Grid Computing resources within the KM3NeT computing model
NCSR Demokritos
a e-mail: cfjs@outlook.com
Published online: 11 April 2016
KM3NeT is a future European deep-sea research infrastructure hosting a new generation neutrino detectors that – located at the bottom of the Mediterranean Sea – will open a new window on the universe and answer fundamental questions both in particle physics and astrophysics. International collaborative scientific experiments, like KM3NeT, are generating datasets which are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. These experiments, in their majority, adopt computing models consisting of different Tiers with several computing centres and providing a specific set of services for the different steps of data processing such as detector calibration, simulation and data filtering, reconstruction and analysis. The computing requirements are extremely demanding and, usually, span from serial to multi-parallel or GPU-optimized jobs. The collaborative nature of these experiments demands very frequent WAN data transfers and data sharing among individuals and groups. In order to support the aforementioned demanding computing requirements we enabled Grid Computing resources, operated by EGI, within the KM3NeT computing model. In this study we describe our first advances in this field and the method for the KM3NeT users to utilize the EGI computing resources in a simulation-driven use-case.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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