CERN Accelerating science

Article
Report number	physics/0306150 ; CHEP-2003-WEPT003
Title	The CMS Event Builder
Related title	COMPACT MUON SOLENOID
Author(s)	Brigljevic, V. ; Bruno, G. ; Cano, E. ; Cittolin, S. ; Csilling, A. ; Gigi, D. ; Glege, F. ; Gomez-Reino, R. ; Gulmini, M. ; Gutleber, J. ; Jacobs, C. ; Kozlovszky, M. ; Larsen, H. ; Magrans de Abril, I. ; Meijers, F. ; Meschi, E. ; Murray, S. ; Oh, A. ; Orsini, L. ; Pollet, L. ; Racz, A. ; Samyn, D. ; Scharff-Hansen, P. ; Schwick, C. ; Sphicas, P. ; O'Dell, V. ; Suzuki, I. ; Berti, L. ; Maron, G. ; Toniolo, N. ; Zangrando, L. ; Ninane, A. ; Erhan, S. ; Bhattacharya, S. ; Branson, J.
Affiliation	(CERN) ; (Fermilab) ; (Legnaro) ; (Louvain-la-Neuve) ; (UCLA) ; (UCSD)
Publication	2003
Imprint	2003-06
Number of pages	mult.
Note	Conference CHEP03 Subj-class: Instrumentation and Detectors
In:	eConf C 0303241 (2003) pp.WEPT003
In:	2003 Conference for Computing in High-Energy and Nuclear Physics, La Jolla, CA, USA, 24 - 28 Mar 2003, pp.WEPT003
Subject category	Other Fields of Physics
Accelerator/Facility, Experiment	CERN LHC ; CMS
Abstract	The data acquisition system of the CMS experiment at the Large Hadron Collider will employ an event builder which will combine data from about 500 data sources into full events at an aggregate throughput of 100 GByte/s. Several architectures and switch technologies have been evaluated for the DAQ Technical Design Report by measurements with test benches and by simulation. This paper describes studies of an EVB test-bench based on 64 PCs acting as data sources and data consumers and employing both Gigabit Ethernet and Myrinet technologies as the interconnect. In the case of Ethernet, protocols based on Layer-2 frames and on TCP/IP are evaluated. Results from ongoing studies, including measurements on throughput and scaling are presented. The architecture of the baseline CMS event builder will be outlined. The event builder is organised into two stages with intelligent buffers in between. The first stage contains 64 switches performing a first level of data concentration by building super-fragments from fragments of 8 data sources. The second stage combines the 64 super-fragments into full events. This architecture allows installation of the second stage of the event builder in steps, with the overall throughput scaling linearly with the number of switches in the second stage. Possible implementations of the components of the event builder are discussed and the expected performance of the full event builder is outlined.

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