Distribution of Random Streams for Simulation Practitioners

International audienceThere is an increasing interest in the distribution of parallel random number streamsin the high-performance computing community particularly, with the manycore shift. Even ifwe have at our disposal statistically sound random number generators according to the latestand thorough testing libraries, their parallelization can still be a delicate problem. Indeed, aset of recent publications shows it still has to be mastered by the scientific community. Withthe arrival of multi-core and manycore processor architectures on the scientist desktop, modelerswho are non-specialists in parallelizing stochastic simulations need help and advice in distributingrigorously their experimental plans and replications according to the state of the art in pseudo-random numbers parallelization techniques. In this paper, we discuss the different partitioningtechniques currently in use to provide independent streams with their corresponding software. Inaddition to the classical approaches in use to parallelize stochastic simulations on regular processors,this paper also presents recent advances in pseudo-random number generation for general-purposegraphical processing units. The state of the art given in this paper is written for simulationpractitioners

Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis

The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts. The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention. Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor. In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified. A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented. The framework is then optimized with respect to computational efficiency. Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed. Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment. The effectiveness and applicability of all used schemes is analyzed. Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework. To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios. As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group. Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models

Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis

The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts. The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention. Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor. In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified. A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented. The framework is then optimized with respect to computational efficiency. Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed. Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment. The effectiveness and applicability of all used schemes is analyzed. Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework. To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios. As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group. Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Improving water network management by efficient division into supply clusters

El agua es un recurso escaso que, como tal, debe ser gestionado de manera eficiente. Así, uno de los propósitos de dicha gestión debiera ser la reducción de pérdidas de agua y la mejora del funcionamiento del abastecimiento. Para ello, es necesario crear un marco de trabajo basado en un conocimiento profundo de la redes de distribución. En los casos reales, llegar a este conocimiento es una tarea compleja debido a que estos sistemas pueden estar formados por miles de nodos de consumo, interconectados entre sí también por miles de tuberías y sus correspondientes elementos de alimentación. La mayoría de las veces, esas redes no son el producto de un solo proceso de diseño, sino la consecuencia de años de historia que han dado respuesta a demandas de agua continuamente crecientes con el tiempo. La división de la red en lo que denominaremos clusters de abastecimiento, permite la obtención del conocimiento hidráulico adecuado para planificar y operar las tareas de gestión oportunas, que garanticen el abastecimiento al consumidor final. Esta partición divide las redes de distribución en pequeñas sub-redes, que son virtualmente independientes y están alimentadas por un número prefijado de fuentes. Esta tesis propone un marco de trabajo adecuado en el establecimiento de vías eficientes tanto para dividir la red de abastecimiento en sectores, como para desarrollar nuevas actividades de gestión, aprovechando esta estructura dividida. La propuesta de desarrollo de cada una de estas tareas será mediante el uso de métodos kernel y sistemas multi-agente. El spectral clustering y el aprendizaje semi-supervisado se mostrarán como métodos con buen comportamiento en el paradigma de encontrar una red sectorizada que necesite usar el número mínimo de válvulas de corte. No obstante, sus algoritmos se vuelven lentos (a veces infactibles) dividiendo una red de abastecimiento grande.Herrera Fernández, AM. (2011). Improving water network management by efficient division into supply clusters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11233Palanci

PariRandom : Entropy distribution in a p2p network

Current software entropy generators bring entropy to a level which is not always suitable for its final use. There is a particular trade-off between the quality of entropy and the speed needed to generate it. We propose a novel method to improve the quality of entropy generators that leverages the ever growing phenomenon of Peer-to-peer networks. PariRandom is a pseudo-random number generation system that may be used to extend all other existing Pseudo Random Number Generation (PRNG) algorithms, ensuring they have an equal or greater level of entropy. An important aspect of our system is that it does not noticeably increase the underlying traffic, "piggybacking" instead on packets that would be transmitted anyway. The theoretical and experimental results demonstrate an increase in performance, which on a wide-scale, comes close to the performance achieved by hardware entropy generators. Moreover, they guarantee resistance to every kind of attack by malicious nodes in the networ