CERN Accelerating science

CERN Document Server Sök i 6 journaler efter:  Sökningen tog 0.55 sekunder. 
1.
Laurelin: Java-native ROOT I/O for Apache Spark / Melo, Andrew Malone (speaker) (Vanderbilt University (US))
Apache Spark is one of the predominant frameworks in the big data space, providing a fully-functional query processing engine, vendor support for hardware accelerators, and performant integrations with scientific computing libraries. One difficulty in adopting conventional big data frameworks to HEP workflows is the lack of support for the ROOT file format in these frameworks. [...]
2021 - 663. Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics External links: Talk details; Event details In : 25th International Conference on Computing in High Energy & Nuclear Physics
2.
CMS distributed data analysis with CRAB3 / Mascheroni, M (INFN, Milan Bicocca ; Milan Bicocca U.) ; Balcas, J (Vilnius U.) ; Belforte, S (INFN, Trieste ; Trieste U.) ; Bockelman, B P (Nebraska U.) ; Hernandez, J M (Madrid, CIEMAT) ; Ciangottini, D (INFN, Perugia ; Perugia U.) ; Konstantinov, P B (Sofiya, Inst. Phys.) ; Silva, J M D (Sao Paulo, IFT) ; Ali, M A B M (Malaya U.) ; Melo, A M (Vanderbilt U.) et al.
The CMS Remote Analysis Builder (CRAB) is a distributed workflow management tool which facilitates analysis tasks by isolating users from the technical details of the Grid infrastructure. Throughout LHC Run 1, CRAB has been successfully employed by an average of 350 distinct users each week executing about 200,000 jobs per day.CRAB has been significantly upgraded in order to face the new challenges posed by LHC Run 2. [...]
FERMILAB-CONF-15-606-CD.- 2015 - 8 p. - Published in : J. Phys.: Conf. Ser. 664 (2015) 062038 IOP Open Access article: PDF;
In : 21st International Conference on Computing in High Energy and Nuclear Physics, Okinawa, Japan, 13 - 17 Apr 2015, pp.062038
3.
Integrating Amazon EC2 with the CMS Production Framework / Melo, Andrew Malone (Vanderbilt U.) /CMS Collaboration
As cloud middleware and cloud providers have become more robust, various experiments with experience in Grid submission have begun to investigate the possibility of taking previously Grid-Enabled applications and making them compatible with Cloud Computing. Successful implementation will allow for dynamic scaling of the available hardware resources, providing access to peak-load handling capabilities and possibly resulting in lower costs to the experiment. [...]
CMS-CR-2011-348.- Geneva : CERN, 2011 - 6 p. Fulltext: PDF;
In : 14th International Workshop On Advanced Computing And Analysis Techniques In Physics Research, Uxbridge, West London, UK, 5 - 9 Sep 2011
4.
The CMS workload management system / Cinquilli, Mattia (UC, San Diego) ; Evans, D (Fermilab) ; Foulkes, Stephen (Fermilab) ; Hufnagel, Dirk (Fermilab) ; Mascheroni, Marco (NICPB, Tallinn) ; Norman, Matthew (UC, San Diego) ; Maxa, Zdenek (Caltech) ; Melo, Andrew Malone (Vanderbilt U.) ; Metson, Simon (Bristol U.) ; Riahi, Hassen (INFN, Perugia ; Perugia U.) et al.
CMS has started the process of rolling out a new workload management system. This system is currently used for reprocessing and monte carlo production with tests under way using it for user analysis. It was decided to combine, as much as possible, the production/processing, analysis and T0 codebases so as to reduce duplicated functionality and make best use of limited developer and testing resources. This system now includes central request submission and management (Request Manager); a task queue for parcelling up and distributing work (WorkQueue) and agents which process requests by interfacing with disparate batch and storage resources (WMAgent)..
CMS-CR-2012-105.- Geneva : CERN, 2012 - 12 p. - Published in : J. Phys.: Conf. Ser. 396 (2012) 032113
Fulltext: PDF;
In : Computing in High Energy and Nuclear Physics 2012, New York, NY, USA, 21 - 25 May 2012, pp.032113
5.
Using Amazon's Elastic Compute Cloud to scale CMS' compute hardware dynamically. / Melo, Andrew Malone (Vanderbilt U.) /CMS Collaboration
Large international scientific collaborations such as the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider have traditionally addressed their data reduction and analysis needs by building and maintaining dedicated computational infrastructure. Emerging cloud-computing services such as Amazon's Elastic Compute Cloud (EC2) offer short-term CPU and storage resources with costs based on usage. [...]
CMS-CR-2011-018.- Geneva : CERN, 2011 - 6 p. Fulltext: PDF;
In : Conference on Computing in High Energy and Nuclear Physics 2010, Taipei, Taiwan, 18 - 22 Oct 2010
6. CMS - The Compact Muon Solenoid / Waltenberger, Winstitute et al.
Approved: 31 January 1996.-
Status: Data Taking
Fulltext: CMS_b - JPG; CMS - JPG; External link: Experiment home page
Experiment: CMS

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