In this paper we propose method for estimating and analysis measurements of delays in the computational cluster interconnection subsystem. Delays are combined into the set of pairs (source, destination). We have measurements of delays extracted by network_test2 utility from interconnections of following supercomputers: BlueGene/P, Lomonosov-1, Lomonosov-2 (Lomonosov MSU) and Jurope (Julich). We have clustered pairs of delays by DBscan and Divisive algorithms. Results of clusterisation revealed that DBScan is more accurate algorithm then divisive and allows to extract clusters, which correspond to the actual features in the supercomputer interconnections. Clusters gather near the same components of supercomputer network infrastructure. Gained clusters were visualized in 2-D by special tool, developed by authors.Introduction. Modern supercomputers are used to solve a wide range of problems, in particular: mathematical modeling and processing of large amounts of data. Supercomputers typically have an architecture of a computer cluster. Computations on supercomputers occur in parallel on many processing elements: cluster nodes equipped with multi-core processors, however, transfers between processors and cluster nodes can significantly slow down the parallel program. In order to minimize application performance losses, it is necessary to understand between which processors the delivery of messages occurs most quickly or vice versa slowly. It is necessary to optimally distribute the computations for the processors making up the supercomputer.
<p><strong>Abstract.</strong> Iris code matching is an important stage of iris biometric systems which compares the input iris code with stored patterns of enrolled iris codes and classifies the code into one of classes so that, the claim is accepted or rejected. Several classifier based approaches are proposed by the researchers to improve the recognition accuracy. In this paper, we discuss the factors affecting an iris classifier’s performance and we propose a reliability index for iris matching techniques to quantitatively measure the extent of system reliability, based on false acceptance rate and false rejection rates using Monte Carlo Simulation. Experiments are carried out on benchmark databases such as, IITD, MMU v-2, CASIA v-4 Distance and UBIRIS v.2.</p>
Abstract. Part-based object representation and part matching problem often appear in various areas of data analysis. A special case of particular interest is when parts are not fully separated, but in relations with each other. The natural way to model such objects are graphs, and part matching problem becomes graph matching problem. Over the years, many methods to solve graph matching problems have been proposed, but it remains relevant due to its complexity. We propose a novel approach to solving graph matching problem based on learning distance metric on graph vertices. We empirically demonstrate that our method outperforms traditional methods based on solving quadratic assignment problem. We also provide an theoretical estimation of computational complexity of proposed method.
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