Physical surveys in many fields use segmented detectors to sense some physical quantities. In many cases, physical phenomena produce raw data with a rate higher than that possible to identify interesting events and organize data when using a single host. This paper proposes a distributed and parallel processing system that greatly reduces the time needed for identifying events and organizing data. Moreover, our solution can be scaled according to the experiment at hand. This opens the possibility to analyse a wider range of physical phenomena, increases the accuracy of observations, and makes it possible to organize widely distributed experiments involving different laboratories or facilities at the same time. The proposed solution consists of a parallel algorithm able to quickly process patterns sensed by a segmented detector, hence achieving identification and data cataloguing. Moreover, a distributed software architecture properly taps into cloud-based resources to handle the massive amount of raw data generated by an experiment. The results in terms of performances for the proposed distributed and parallel solution have shown a relevant speed up for the required processing.

A Cloud-Distributed GPU Architecture for Pattern Identification in Segmented Detectors Big-Data Surveys

NAPOLI, CHRISTIAN;PAPPALARDO, Giuseppe;TRAMONTANA, EMILIANO ALESSIO;
2014

Abstract

Physical surveys in many fields use segmented detectors to sense some physical quantities. In many cases, physical phenomena produce raw data with a rate higher than that possible to identify interesting events and organize data when using a single host. This paper proposes a distributed and parallel processing system that greatly reduces the time needed for identifying events and organizing data. Moreover, our solution can be scaled according to the experiment at hand. This opens the possibility to analyse a wider range of physical phenomena, increases the accuracy of observations, and makes it possible to organize widely distributed experiments involving different laboratories or facilities at the same time. The proposed solution consists of a parallel algorithm able to quickly process patterns sensed by a segmented detector, hence achieving identification and data cataloguing. Moreover, a distributed software architecture properly taps into cloud-based resources to handle the massive amount of raw data generated by an experiment. The results in terms of performances for the proposed distributed and parallel solution have shown a relevant speed up for the required processing.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/17282
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