The GPU Virtualization Service (gVirtuS) presented in this work tries to fill the gap between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for-use high performance virtual clusters deployed via public or private computing clouds. gVirtuS allows an instanced virtual machine to access GPGPUs in a transparent and hypervisor independent way, with an overhead slightly greater than a real machine/GPGPU setup. The performance of the components of gVirtuS is assessed through a suite of tests in different deployment scenarios, such as providing GPGPU power to cloud computing based HPC clusters and sharing remotely hosted GPGPUs among HPC nodes

Abstract. In this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides.We first introduce the theoretical basis (with its roots sinking into the Bayes theorem) and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b).This approach also provides a way to systematically reduce (and quantify) model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner.

[1] The aim of this work is to explore the effectiveness of theoretical information approaches for the reduction of data complexity in multimodel ensemble systems. We first exploit a weak form of independence, i.e. uncorrelation, as a mechanism for detecting linear relationships. Then, stronger and more general forms of independence measure, such as mutual information, are used to investigate dependence structures for model selection. A distance matrix, measuring the interdependence between data, is derived for the investigated measures, with the scope of clustering correlated/dependent models together. Redundant information is discarded by selecting a few representative models from each cluster. We apply the clustering analysis in the context of atmospheric dispersion modeling, by using the ETEX-1 data set. We show how the selection of a small subset of models, according to uncorrelation or mutual information distance criteria, usually suffices to achieve a statistical performance comparable to, or even better than, that achieved from the whole ensemble data set, thus providing a simpler description of ensemble results without sacrificing accuracy.Citation: Riccio A., A. Ciaramella, G. Giunta, S. Galmarini, E. Solazzo, and S. Potempski (2012), On the systematic reduction of data complexity in multimodel atmospheric dispersion ensemble modeling,

A software package based on a modification of the Weeks' method is presented for calculating function values f ( t ) of the inverse Laplace transform. This method requires transform values F ( z ) at arbitrary points in the complex plane, and is suitable when f ( t ) has continuous derivatives of all orders; it is especially attractive when f ( t ) is required at a number of different abscissas t .

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