EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||6|
|Section||7 - Facilities, Clouds and Containers|
|Published online||16 November 2020|
Standalone containers with ATLAS offline software
Faculty of Mathematics and Natural Sciences, University of Wuppertal, Gausstrasse 20, 42119 Wuppertal, Germany
2 Department of Physics and Astronomy, University of Iowa, 203 Van Allen Hall, Iowa City, IA 522421479, USA
3 School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
4 European Organization for Nuclear Research (CERN), CH-1211 Geneve 23, Switzerland
* e-mail: email@example.com
Copyright 2020 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license.
Published online: 16 November 2020
This paper describes the deployment of the offline software of the ATLAS experiment at LHC in containers for use in production workflows such as simulation and reconstruction. To achieve this goal we are using Docker and Singularity, which are both lightweight virtualization technologies that can encapsulate software packages inside complete file systems. The deployment of offline releases via containers removes the interdependence between the runtime environment needed for job execution and the configuration of the computing nodes at the sites. Docker or Singularity would provide a uniform runtime environment for the grid, HPCs and for a variety of opportunistic resources. Additionally, releases may be supplemented with a detector’s conditions data, thus removing the need for network connectivity at computing nodes, which is normally quite restricted for HPCs. In preparation to achieve this goal, we have built Docker and Singularity images containing single full releases of ATLAS software for running detector simulation and reconstruction jobs in runtime environments without a network connection. Unlike similar efforts to produce containers by packing all possible dependencies of every possible workflow into heavy images (≈ 200GB), our approach is to include only what is needed for specific workflows and to manage dependencies efficiently via software package managers. This approach leads to more stable packaged releases where the dependencies are clear and the resulting images have more portable sizes ( 16GB). In an effort to cover a wider variety of workflows, we are deploying images that can be used in raw data reconstruction. This is particularly challenging due to the high database resource consumption during the access to the experiment’s conditions payload when processing data. We describe here a prototype pipeline in which images are provisioned only with the conditions payload necessary to satisfy the jobs’ requirements. This database-on-demand approach would keep images slim, portable and capable of supporting various workflows in a standalone fashion in environments with no network connectivity.
© The Authors, published by EDP Sciences, 2020
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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