PAMIHR. A Parallel FORTRAN Program for Multidimensional Quadrature on Distributed Memory Architectures

Abstract. PAMIHR: a parallel adaptive routine for the approximate computation of a multidimensional integral over a hyperrectangular region is described. The software is designed to efficiently run on a MIMD distributed memory environment, and it's based on the widely diffused communication system BLACS. PAMIHR, further, gives special attention to the problems of scalability and of load balancing among the processes

Incontri matematici del terzo tipo

Il Piano Lauree Scientifiche (PLS) `e oggi il principale programma di orientamento universitario in ambito scientifico operante in Italia. Esso nasce con il nome di Progetto Lauree Scientifiche nel 2004 dalla collaborazione tra il Ministero dell’Istruzione, dell’Universit`a e della Ricerca (MIUR), la Conferenza dei Presidi di Scienze e Teconologia (Con.Scienze), e Confindustria, con l’obiettivo principale di fare fronte alla riduzione degli studenti iscritti ai corsi di laurea scientifici, principalmente in matematica, fisica e chimic

Scalability and Load Balancing in Adaptive Algorithms for Multidimensional Integration

A parallel adaptive algorithm for the approximate computation of a multi-dimensional integral over an hyperrectangular region is described. This is a more general version of the well known algorithms presented in Genz (1987) and Lapegna and D'Alessio (1993) and it has been developed for an efficient implementation on a MIMD distributed memory multiprocessor. In order to achieve a good scalability, all global communications have been removed from the algorithm. The processor's network has been configured as a multidimensional periodical mesh, to distribute information about the integrand behavior fast enough in order to achieve a good load balancing. Test results on the Intel Touchstone Delta System are given

Toward a high-performance clustering algorithm for securing edge computing environments

Clustering algorithms are efficient tools for discov-ering correlations or affinities within large datasets and are the basis of several Machine Learning processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge Computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the networks to detect or prevent, as an example, attacks from insecure domains. In such a context, the present work introduces a new hybrid clustering algorithm for Edge Computing environments that can classify edge nodes taking into account their reliability. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end G PU-based computing systems. The achieved results confirm the possibility of designing intelligent sensors networks where decisions are taken at the data collection points