Browsing by Author "Kama, S."
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- An evaluation of GPUs for use in an upgraded ATLAS High Level TriggerPublication . Delgado, A.T.; Conde Muno, P.; Soares, J. Augusto; Goncalo, R.; Baines, J.; Bold, T.; Emeliyanov, D.; Kama, S.; Bauce, M.; Messina, A.; Negrini, M.; Sidoti, A.; Rinaldi, L.; Tupputi, S.; Greenwood, Z.D.; Elliott, A.; Laosooksathit, S.ATLAS is a general purpose particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system consists of two levels, the first level (L1) implemented in hardware and the High Level Trigger (HLT) implemented in software running on a computing cluster of commodity CPUs. The HLT reduces the trigger rate from the 100 kHz L1 accept rate to 1 kHz for recording, requiring an average per-event processing time of ~300 ms for this task. The HLT selection is based on reconstructing tracks in the Inner Detector and Muon Spectrometer and clusters of energy deposited in the calorimeters (electromagnetic and hadronic). Performing this reconstruction within the available HLT computing cluster resources presents a significant challenge. Future HLT upgrades will result in higher detector occupancies and, consequently, will harden the reconstruction constraints. General purpose Graphics Processor Units (GPGPU) are being evaluated for possible future inclusion in an upgraded HLT computing cluster. We report on a demonstrator that has been developed consisting of GPGPU implementations of the calorimeters clustering and Inner Detector and Muon tracking algorithms integrated within the HLT software framework. We give a brief overview of the algorithm implementation and present preliminary measurements comparing the performance of the GPGPU algorithms with the current CPU versions.
- Triggering events with GPUs at ATLASPublication . Kama, S.; Soares, J.Augusto; Baines, J.; Bauce, M.; Bold, T.; Muino, P.Conde; Emeliyanov, D.; Goncalo, R.; Messina, A.; Negrini, M.; Rinaldi, L.; Sidoti, A.; Delgado, A.Tavares; Tupputi, S.; Lopes, L.Vaz GilThe growing complexity of events produced in LHC collisions demands increasing computing power both for the online selection and for the offline reconstruction of events. In recent years there have been significant advances in the performance of Graphics Processing Units (GPUs) both in terms of increased compute power and reduced power consumption that make GPUs extremely attractive for use in a complex particle physics experiments such as ATLAS. A small scale prototype of the full ATLAS High Level Trigger has been implemented that exploits reconstruction algorithms optimized for this new massively parallel paradigm. We discuss the integration procedure followed for this prototype and present the performance achieved and the prospects for the future.