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E’ coordinatore della Divisione Biometria del Pattern Recognition and Applications Laboratory (PRA Lab) diretto dal Prof. Fabio Roli. L’attività di ricerca è incentrata sulle tecnologie biometriche per la sicurezza informatica. In particolare si occupa di classificazione e verifica di impronte digitali e volti, rilevazione di contraffazioni e sistemi multimodali. Ha al suo attivo oltre ottanta pubblicazioni fra riviste, atti di conferenze e congressi, capitoli di libro, tutte di impatto internazionale.

E’ revisore di progetti, riviste e conferenze internazionali.

E’ team leader e responsabile di progetti di ricerca internazionali pubblici (FP-European Union) e privati (Crossmatch) nonché progetti nazionali (PRIN, RAS) e locali (“Giovani Ricercatori”) e di collaborazione con il RaCIS di Cagliari.

L’elenco completo delle sue pubblicazioni, delle tesi di laurea e dottorato delle quali è stato co-relatore è nel suo curriculum_vitae e nella pagina personale del sito PRA Lab.

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He is team leader of the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) leaded by Prof. Fabio Roli. His research activity is focused on the biometric Technologies for information security. In particular, identification, verification and vulnerability analysis of fingerprint and face, multi-modal biometric systems. He has co-authored more than one hundred of publications in journal, conference proceedings and books chapters. He also co-authored the voice “Antispoofing: Multimodal” in the last edition of Encyclopedia of Biometrics.

He acts as referee for international projects, journals and conferences.

He is in charge of national and international research projects.

The complete publication list and activities is in his curriculum_vitae and in his personal webpage in the PRA Lab website.

Titolo: CUDA-quicksort: An improved GPU-based implementation of quicksort
Autori: 
Data di pubblicazione: 2015
Rivista: 
CONCURRENCY AND COMPUTATION  
Abstract: Sorting is a very important task in computer science and becomes a critical operation for programs that make heavy use of sorting algorithms, in particular when dealing with huge amounts of data. Generalpurpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize some sorting algorithms. Two GPU-based implementations of the quicksort algorithm were presented in literature: the GPU-quicksort, a CUDA iterative implementation, and the NVIDIA CUDA Dynamic Parallel (CDP) advanced quicksort, a recursive implementation. In this article we propose CUDA-Quicksort a new blockoriented iterative GPU-based implementation of the sorting algorithm. CUDA-Quicksort has been designed starting from GPU-Quicksort. Unlike GPU-Quicksort, it uses atomic primitives to perform inter-block communications while ensuring an optimized access to the GPU memory. Experiments performed on six sorting benchmark distributions show that CUDA-Quicksort is up to four times faster than the iterative GPU-Quicksort and up to three times faster than the recursive NVIDIA CDPQuicksort. An in depth analysis of the performance between the proposed CUDA-Quicksort and the GPUQuicksort show that the main improvement is related to the optimized GPU memory access rather than to the use of the atomic primitives. Moreover, with the aim to assess the advantages of using the CUDA dynamic parallelism, we also implemented a recursive version of the CUDA-Quicksort. Experimental results show that the proposed implementation is faster than the one provided by NVIDIA, with better performance achieved using the iterative implementation.
Handle: http://hdl.handle.net/11584/134254
Tipologia:1.1 Articolo in rivista

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