Integration of the Trigger and Data Acquisition systems in ATLAS

Riu, I.; Abolins, M.; Adragna, P.; Avolio, G.; Backlund, S.; Badescu, E.; Batreanu, S.; Battaglia, A.; Beck, H. P.; Bee, C. P.; Bell, Peter A.; Blair, Robert; Bogaerts, A.; Bołd, T.; Bosman, M.; Boyd, J.; Burckhart-Chromek, D.; Caprini, M.; Cimino, D.; Ciobotaru, M. D.; Corso-Radu, A.; Costa, Manuel; Torres, R. Coura; Cranfield, R.; Crone, G.; Dawson, J. W.; Simões, Jorge; DellaPietra, M.; Demers, S.; Mattia, A. Di; Dobson, M.; Anjos, A. Dos; Dotti, A.; Drake, G.; Ellis, N.; Ermoline, Y.; Eschrich, I.; Ferland, J.; Ferrari, R.; Ferrer, M. L.; Francis, D.; Gadomski, S.; Gameiro, S.; Garitaonandia, H.; Gaudio, Giovanni; Gorini, B.; Gowdy, S. J.; Green, B.; Haberichter, W.; Hadavand, H. K.; Haeberli, C.; Hauschild, M.; Hauser, R.; Hillier, S. J.; Hinkelbein, C.; Hughes-Jones, R.; Idarraga, J.; Joos, M.; Kazarov, A.; Kekoe, R.; Kieft, G.; Kirk, J.; Kolos, S.; Kordas, K.; Korcyl, K.; Kugel, A.; Leahu, L.; Leahu, M.; Miotto, G. Lehmann; Lellouch, D.; Mapelli, L.; Martin, Brent; Mašík, J.; McPherson, R. A.; Meessen, C.; Meiroşu, Cătălin; Mineev, M.; Misiejuk, A.; Mornacchi, G.; Mueller, Mario H.; Garcia, Raul Murillo; Nagasaka, Y.; Negri, A.; Aranda, C. Padilla; Parodi, Franco; Pasqualucci, E.; Pauly, T.; Petersen, J.; Pope, B. G.; Renkel, P.; Roda, C.; Salvatore, D.; Scannicchio, D.; Schiavi, Chiara; Schlereth, J.; Scholtes, Ingo; Sivoklokov, S. Yu.; Sloper, J.; Soloviev, I.; Spiwoks, R.; Stancu, S.; Stelzer, J.; Strong, J. A.; Sushkov, S.; Tremblet, L.; Ünel, G.; Vandelli, W.; Vermeulen, J. C.; Schmitt, Jonas; Werner, P.; Wheeler-Ellis, S. J.; Wickens, F. J.; Wiedenmann, W.; Wilkens, H. G.; Winklmeier, F.; Wu, X.; Yasu, Y.; Zema, F.; Zhang, J.; Zobernig, H.

doi:10.1109/rtc.2007.4382844

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…This filtering system comprises three cascaded operation levels, applying successive cuts to the incoming data (Riu et al, 2008). The first level (L1) will receive full data and will reduce the input event rate to ~75 kHz.…”

Section: The Applicationmentioning

confidence: 99%

Segmented Online Neural Filtering System Based On Independent Components Of Pre-Processed Information

Torres¹,

Filho²,

Lima³

et al. 2010

Signal Processing

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Section: The Applicationmentioning

confidence: 99%

Segmented Online Neural Filtering System Based On Independent Components Of Pre-Processed Information

Torres¹,

Filho²,

Lima³

et al. 2010

Signal Processing

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“…The user then connects via a light-weigh client to the RPC servers in the reserved nodes to start the PMG agents. 1 Finally the user initiates a TDAQ session that will connect to these PMG agents and run the TDAQ transparently, as if running on a dedicated cluster.…”

Section: B Start Run Monitor Stopmentioning

confidence: 99%

“…Many different types of test are performed such as functionality, performance and stability testing. These take place at every level of the system, for example unit testing of individual software applications, integration tests which verify the compatibility and correct functionality of software applications within a complete sub-system [1], [2] and large-scale testing where the system is tested at a scale approaching the final size of ATLAS. Generally the unit and integration testing has been carried out on small (of the order 50 machines) testbeds dedicated to TDAQ activities located at CERN or collaborating laboratories.…”

Section: Introductionmentioning

confidence: 99%

Using the Grid to Test the ATLAS Trigger and Data Acquisition System at Large Scale

Forti¹,

Garitaonandia²,

Masik³

et al. 2007

2007 15th IEEE-NPSS Real-Time Conference

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Abstract-The ATLAS Trigger and Data Acquisition System has been designed to use more than 2000 CPUs. During the current development stage it is crucial to test the system on a number of CPUs of similar scale. A dedicated farm of this size is difficult to find, and can only be made available for short periods. On the other hand many large farms have become available recently as part of computing grids, leading to the idea of using them to test the TDAQ system of ATLAS. However the task of adapting the TDAQ system to run on the Grid is not trivial, as it requires full access to the computing resources it runs on and real-time interaction. Moreover the Grid virtualises the resources to present a common interface to the user. We will describe the implementation and first tests of a scheme that resolves these issues using a pilot job mechanism. The Tier2 cluster in Manchester was successfully used to run a full TDAQ system on 400 nodes using this implementation. Apart from the tests described above, this scheme also has great potential for other applications, like running Grid remote farms to perform detector calibration and monitoring in real-time, and automatic nightly testing of the TDAQ system.

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“…T HE ATLAS trigger and data-acquisition (TDAQ) system is based on three levels of online event selection [1], [2]. Each trigger level refines the decisions made at the previous level and, where necessary, applies additional selection criteria.…”

Section: Introductionmentioning

confidence: 99%

Performance of the Final Event Builder for the ATLAS Experiment

Beck

Abolins

Battaglia

et al. 2008

IEEE Trans. Nucl. Sci.

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Abstract-Event data from proton-proton collisions at the LHC will be selected by the ATLAS experiment in a three level trigger system, which reduces the initial bunch crossing rate of 40 MHz at its first two trigger levels (LVL1+LVL2) to ∼ 3 kHz. At this rate the Event-Builder collects the data from all Read-Out system PCs (ROSs) and provides fully assembled events to the the EventFilter (EF), which is the third level trigger, to achieve a further rate reduction to ∼ 200 Hz for permanent storage. The EventBuilder is based on a farm of O(100) PCs, interconnected via Gigabit Ethernet to O(150) ROSs. These PCs run Linux and multi-threaded software applications implemented in C++. All the ROSs and one third of the Event-Builder PCs are already installed and commissioned. We report on performance tests on this initial system, which show promising results to reach the final data throughput required for the ATLAS experiment.

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Integration of the Trigger and Data Acquisition systems in ATLAS

Cited by 5 publications

References 5 publications

Segmented Online Neural Filtering System Based On Independent Components Of Pre-Processed Information

Segmented Online Neural Filtering System Based On Independent Components Of Pre-Processed Information

Using the Grid to Test the ATLAS Trigger and Data Acquisition System at Large Scale

Performance of the Final Event Builder for the ATLAS Experiment

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