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Storage Element performance optimization for CMS analysis jobs
Tier-2 computing sites in the Worldwide Large Hadron Collider Computing Grid (WLCG) host CPU-resources (Compute Element, CE) and storage resources (Storage Element, SE). The vast amount of data that needs to processed from the Large Hadron Collider (LHC) experiments requires good and efficient use o...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2012
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/396/4/042037 http://cds.cern.ch/record/1457866 |
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author | Behrmann, Gert Dahlblom, Jonas Guldmyr, Johan Happonen, Kalle Linden, Tomas |
author_facet | Behrmann, Gert Dahlblom, Jonas Guldmyr, Johan Happonen, Kalle Linden, Tomas |
author_sort | Behrmann, Gert |
collection | CERN |
description | Tier-2 computing sites in the Worldwide Large Hadron Collider Computing
Grid (WLCG) host CPU-resources (Compute Element, CE) and storage
resources (Storage Element, SE). The vast amount of data that needs to
processed from the Large Hadron Collider (LHC) experiments requires good
and efficient use of the available resources. Having a good CPU
efficiency for the end users analysis jobs requires that the performance
of the storage system is able to scale with I/O requests from hundreds
or even thousands of simultaneous jobs.
In this presentation we report on the work on improving the SE
performance at the Helsinki Institute of Physics (HIP) Tier-2 used for
the Compact Muon Experiment (CMS) at the LHC. Statistics from CMS grid
jobs are collected and stored in the CMS Dashboard for further analysis,
which allows for easy performance monitoring by the sites and by the CMS
collaboration. As part of the monitoring framework CMS uses the JobRobot
which sends every four hours 100 analysis jobs to each site. CMS also
uses the HammerCloud (HC) tool for site monitoring and stress testing
and HC has replaced the JobRobot. The performance of the
analysis workflow submitted with JobRobot or HC can be used to track the
performance due to site configuration changes, since the analysis
workflow is kept the same for all sites and for months in time. The CPU
efficiency of the JobRobot jobs at HIP was increased approximately by 50
% to more than 90 %, by tuning the SE and by improvements in the CMSSW
and dCache software. The performance of the CMS analysis jobs improved
significantly too. Similar work has been done on other CMS Tier-sites,
since on average the CPU efficiency for CMSSW jobs has increased during
2011. Better monitoring of the SE allows faster detection of problems,
so that the performance level can be kept high. The next storage upgrade
at HIP will consist of SAS disk enclosures which can be stress tested on
demand with HC workflows, to make sure that the I/O-performance is good. |
id | cern-1457866 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2012 |
record_format | invenio |
spelling | cern-14578662019-09-30T06:29:59Zdoi:10.1088/1742-6596/396/4/042037http://cds.cern.ch/record/1457866engBehrmann, GertDahlblom, JonasGuldmyr, JohanHapponen, KalleLinden, TomasStorage Element performance optimization for CMS analysis jobsDetectors and Experimental TechniquesTier-2 computing sites in the Worldwide Large Hadron Collider Computing Grid (WLCG) host CPU-resources (Compute Element, CE) and storage resources (Storage Element, SE). The vast amount of data that needs to processed from the Large Hadron Collider (LHC) experiments requires good and efficient use of the available resources. Having a good CPU efficiency for the end users analysis jobs requires that the performance of the storage system is able to scale with I/O requests from hundreds or even thousands of simultaneous jobs. In this presentation we report on the work on improving the SE performance at the Helsinki Institute of Physics (HIP) Tier-2 used for the Compact Muon Experiment (CMS) at the LHC. Statistics from CMS grid jobs are collected and stored in the CMS Dashboard for further analysis, which allows for easy performance monitoring by the sites and by the CMS collaboration. As part of the monitoring framework CMS uses the JobRobot which sends every four hours 100 analysis jobs to each site. CMS also uses the HammerCloud (HC) tool for site monitoring and stress testing and HC has replaced the JobRobot. The performance of the analysis workflow submitted with JobRobot or HC can be used to track the performance due to site configuration changes, since the analysis workflow is kept the same for all sites and for months in time. The CPU efficiency of the JobRobot jobs at HIP was increased approximately by 50 % to more than 90 %, by tuning the SE and by improvements in the CMSSW and dCache software. The performance of the CMS analysis jobs improved significantly too. Similar work has been done on other CMS Tier-sites, since on average the CPU efficiency for CMSSW jobs has increased during 2011. Better monitoring of the SE allows faster detection of problems, so that the performance level can be kept high. The next storage upgrade at HIP will consist of SAS disk enclosures which can be stress tested on demand with HC workflows, to make sure that the I/O-performance is good.CMS-CR-2012-097oai:cds.cern.ch:14578662012-05-15 |
spellingShingle | Detectors and Experimental Techniques Behrmann, Gert Dahlblom, Jonas Guldmyr, Johan Happonen, Kalle Linden, Tomas Storage Element performance optimization for CMS analysis jobs |
title | Storage Element performance optimization for CMS analysis jobs |
title_full | Storage Element performance optimization for CMS analysis jobs |
title_fullStr | Storage Element performance optimization for CMS analysis jobs |
title_full_unstemmed | Storage Element performance optimization for CMS analysis jobs |
title_short | Storage Element performance optimization for CMS analysis jobs |
title_sort | storage element performance optimization for cms analysis jobs |
topic | Detectors and Experimental Techniques |
url | https://dx.doi.org/10.1088/1742-6596/396/4/042037 http://cds.cern.ch/record/1457866 |
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