Archivio Istituzionale della RicercaGraphics Processing Units (GPUs) have a huge number of cores to speed up graphical computations and they are being used in a wide area of general-purpose applications that require high performances. In this paper, GPU computing is exploited to model the signal propagation and the interference in large RFID systems, which are a promising solution for achieving pervasive computing since they offer the automatic object identification. The speedup of the parallel algorithm is evaluated with respect to a sequential version. Two popular frameworks for general-purpose computing on GPU are considered in the comparison, i.e. CUDA and OpenCL, and distinct implementations are provided for them, highlighting their differences in code optimization and performance.
A comparison of graphics processor architectures for RFID simulation / Ferrero, Renato; Montrucchio, Bartolomeo; Lorenzo, David; Kargar, Ebrahim; Luca, Graglia; Giovanni di Dio, Iovino; Marco, Ribero. - ELETTRONICO. - (2014), pp. 8-14. (Intervento presentato al convegno 2014 17th International Conference on Network-Based Information Systems (NBiS) tenutosi a Salerno (Italia) nel 10-12 Settembre 2014) [10.1109/NBiS.2014.37].
A comparison of graphics processor architectures for RFID simulation
FERRERO, RENATO;MONTRUCCHIO, BARTOLOMEO;
2014
Abstract
Graphics Processing Units (GPUs) have a huge number of cores to speed up graphical computations and they are being used in a wide area of general-purpose applications that require high performances. In this paper, GPU computing is exploited to model the signal propagation and the interference in large RFID systems, which are a promising solution for achieving pervasive computing since they offer the automatic object identification. The speedup of the parallel algorithm is evaluated with respect to a sequential version. Two popular frameworks for general-purpose computing on GPU are considered in the comparison, i.e. CUDA and OpenCL, and distinct implementations are provided for them, highlighting their differences in code optimization and performance.
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https://hdl.handle.net/11583/2582144
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