Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows
In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads t...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_05025.pdf |
| _version_ | 1818891173059624960 |
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| author | Rieger Marcel |
| author_facet | Rieger Marcel |
| author_sort | Rieger Marcel |
| collection | DOAJ |
| description | In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the opensource pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source. It is successfully used in tt̄H cross section measurements and searches for di-Higgs boson production with the CMS experiment. |
| first_indexed | 2024-12-19T17:36:36Z |
| format | Article |
| id | doaj.art-8ebfed45059f4306aeb192c19b6eb7fc |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-19T17:36:36Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-8ebfed45059f4306aeb192c19b6eb7fc2022-12-21T20:12:19ZengEDP SciencesEPJ Web of Conferences2100-014X2020-01-012450502510.1051/epjconf/202024505025epjconf_chep2020_05025Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis WorkflowsRieger Marcel0CERNIn particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the opensource pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source. It is successfully used in tt̄H cross section measurements and searches for di-Higgs boson production with the CMS experiment.https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_05025.pdf |
| spellingShingle | Rieger Marcel Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows EPJ Web of Conferences |
| title | Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows |
| title_full | Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows |
| title_fullStr | Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows |
| title_full_unstemmed | Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows |
| title_short | Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows |
| title_sort | design pattern for analysis automation on distributed resources using luigi analysis workflows |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_05025.pdf |
| work_keys_str_mv | AT riegermarcel designpatternforanalysisautomationondistributedresourcesusingluigianalysisworkflows |