会议论文详细信息
20th International Conference on Computing in High Energy and Nuclear Physics
Compute farm software for ATLAS IBL calibration
物理学;计算机科学
Bindi, M.^1 ; Flick, T.^2 ; Grosse-Knetter, J.^3 ; Heim, T.^2 ; Hsu, S.-C.^5 ; Kretz, M.^4 ; Kugel, A.^4 ; Marx, M.^5 ; Morettini, P.^6 ; Potamianos, K.^7 ; Takubo, Y.^8
Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy^1
Fachbereich C Physik, Bergische Universität Wuppertal, Wuppertal, Germany^2
II Physikalisches Institut, Georg-August-Universität, Göttingen, Germany^3
ZITI Institut für Technische Informatik, Ruprecht-Karls-Universität Heidelberg, Mannheim, Germany^4
Department of Physics, University of Washington, Seattle
WA, United States^5
INFN Sezione di Genova, Genova, Italy^6
Physics Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, United States^7
KEK, High Energy Accelerator Research Organization, Tsukuba, Japan^8
关键词: ATLAS experiment;    Commodity hardware;    Fitting algorithms;    Flexible adjustment;    Modular designs;    Monitoring purpose;    Software and hardwares;    Time over thresholds;   
Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/513/5/052016/pdf
DOI  :  10.1088/1742-6596/513/5/052016
学科分类:计算机科学(综合)
来源: IOP
PDF
【 摘 要 】

In 2014 the Insertable B-Layer (IBL) will extend the existing Pixel Detector of the ATLAS experiment at CERN by over 12 million additional pixels. For calibration and monitoring purposes, occupancy and time-over-threshold data are being histogrammed in the read-out hardware. Further processing of the histograms happens on commodity hardware, which not only requires the fast transfer of histogram data from the read-out hardware to the computing farm via Ethernet, but also the integration of the software and hardware into the already existing data-acquisition and calibration framework (TDAQ and PixelDAQ) of the ATLAS experiment and the current Pixel Detector. We implement the software running on the compute cluster with an emphasis on modularity, allowing for flexible adjustment of the infrastructure and a good scalability with respect to the number of network interfaces, available CPU cores, and deployed machines. By using a modular design we are able to not only employ CPU-based fitting algorithms, but also have the possibility to take advantage of the performance offered by a GPU-based approach to fitting.

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