Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
---|---|---|
Article Number | 05014 | |
Number of page(s) | 7 | |
Section | T5 - Software development | |
DOI | https://doi.org/10.1051/epjconf/201921405014 | |
Published online | 17 September 2019 |
https://doi.org/10.1051/epjconf/201921405014
Improvements to the LHCb software performance testing infrastructure using message queues and big data technologies
1
University of Chinese Academy of Sciences,
Beijing,
China
2
CERN, European Organization for Nuclear Research,
Geneva,
Switzerland
* e-mail: Maciej.Szymanski@cern.ch
** e-mail: Ben.Couturier@cern.ch
Published online: 17 September 2019
Software is an essential component of High Energy Physics experiments. Due to the fact that it is upgraded on relatively short timescales, software provides flexibility, but at the same time is susceptible to issues introduced during development process, thus mandating systematic testing. We present recent improvements to LHCbPR, the framework implemented at LHCb to measure physics and computational performance of complete applications. This infrastructure is essential for keeping track of the optimisation activities related to the upgrade of computing systems which is crucial to meet the requirements of the LHCb detector upgrade for the next stage of data taking of the LHC. Latest developments in LHCbPR include application of messaging system to trigger the tests right after the corresponding software version is built within LHCb nightly builds infrastructure. We will also report on the investigation of using big data technologies in LHCbPR. We have found that using tools such as Apache Spark and Hadoop Distributed File System may significantly improve the functionality of the framework, providing an interactive exploration of the test results with efficient data filtering and flexible development of reports.
© The Authors, published by EDP Sciences, 2019
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.