Calculs parallèles pour le traitement des images satellites

The study presented in this Phd thesis puts in relation two a priori distinct scientific domains that are geology and computer science. Indeed, the context of this work is to design a complete processing line of parallel tools about satellite images, going from the three dimensional reconstruction to the visualization of the retrieved grounds. This work has then implied a strong cooperation with the department of geology at the École Normale Supérieure of Lyon.We propose on one side, the parallelization of an algorithm for three dimensional reconstruction of the relief from a couple of satellite images, and on another side, a parallel algorithm to visualize textured grounds. These works are then related to different domains of computer science like parallelism, stereo vision and image synthesis. A methodological and more general study over geometrical image transformation algorithms is also presented.Concerning the sequential level, we propose for each of the algorithms seen and when it is relevant, different optimizations allowing to reduce the complexity and then the computation times, and some choices for the computational tools to use for increasing the quality of the results, which is a major point in the domain of stereo vision. Concerning the parallelism, we focus on the communications and the load balancing strategies that could be used to take the most advantage of the parallel machines. By comparing our problems to those already studied in the literature, we have arrived at the conclusion that a data driven load balancing was the best suited technique. Moreover, whatever considering the vision or synthesis part, the load balancing problem can be seen exactly in the same way. We can then apply the same strategy over these different algorithms. Finally, a theoretical study of the complexity of our parallel stereo vision algorithm allows us to predict the number of processors necessary to get the best absolute performances for a given input data set.Experimental results done on several parallel machines, Volvox, Cray T3D or Cray T3E allow us to verify the behavior of our parallel algorithms and to confirm their efficiency.L'étude réalisée dans cette thèse met en relation deux domaines scientifiques, a priori distincts, que sont la géologie et l'informatique. En effet, le contexte de ce travail est de concevoir une chaîne complète de traitements parallèles sur les images satellites allant de la reconstruction tridimensionnelle à la visualisation des terrains ainsi reconstitués. Ce travail a donc fait l'objet d'une coopération étroite avec le département de géologie de l'École Normale Supérieure de Lyon.Nous proposons d'une part, la parallélisation d'un algorithme de reconstruction tridimensionnelle de relief à partir d'un couple d'images satellite, et d'autre part, un algorithme parallèle de visualisation de terrains avec texture. Ces travaux font donc appel à plusieurs domaines de l'informatique tels que le parallélisme, la vision stéréoscopique et la synthèse d'images. Une étude méthodologique plus générale sur les algorithmes de transformation géométrique des images est également présentée.Au niveau séquentiel, nous proposons pour chacun des algorithmes abordés et lorsque cela est pertinent, différentes optimisations originales permettant des améliorations en termes de complexité et donc de temps de calculs, ainsi que des choix d'outils calculatoires pouvant améliorer la qualité des résultats, point très sensible dans un domaine comme la vision stéréoscopique. Dans le cadre du parallélisme, nous nous focalisons sur les stratégies de communications et d'équilibrage des charges pouvant être mises en \oe uvre pour tirer le meilleur parti des machines parallèles. En comparant nos problèmes avec ceux déjà traités dans la littérature, nous sommes arrivés à la conclusion qu'un équilibrage des charges dirigé par les données était préférable à toute autre technique. De plus, que l'on se place dans la partie vision ou synthèse, l'équilibrage des charges peut être abordé exactement de la même manière. On peut donc appliquer la même stratégie sur ces différents algorithmes. Enfin, une étude théorique de la complexité de l'algorithme parallèle de vision stéréoscopique nous permet de déduire les points clés influençant les performances et donc d'estimer a priori le nombre de processeurs nécessaires pour obtenir les meilleures performances absolues pour un ensemble connu de données.Des expérimentations menées sur différentes machines parallèles, Volvox, Cray T3D ou Cray T3E nous permettent de vérifier le bon comportement de nos algorithmes parallèles et de confirmer leur efficacité

Impact of Asynchronism on GPU Accelerated Parallel Iterative Computations

International audienceWe study the impact of asynchronism on parallel iterative algorithms in the particular context of local clusters of workstations including GPUs. The application test is a classical PDE problem of advection-diffusion-reaction in 3D. We propose an asynchronous version of a previously developed PDE solver using GPUs for the inner computations. The algorithm is tested with two kinds of clusters, a homogeneous one and a heterogeneous one (with different CPUs and GPUs)

An efficient and robust decentralized algorithm for detecting the global convergence in asynchronous iterative algorithms

URL : http://vecpar.fe.up.pt/2008/papers/25.pdfInternational audienceIn this paper we present a practical, efficient and robust algorithm for detecting the global convergence in any asynchronous iterative process. A proven theoretical version, together with a first practical version, was presented in [1]. However, the main drawback of that first practical version was to require the determination of the maximal communication time between any couple of nodes in the system during the entire iterative process. The version presented in this paper does not require any additional information on the parallel system while always ensuring correct detections

Dynamic Load Balancing and Efficient Load Estimators for Asynchronous Iterative Algorithms

In a previous paper~\cite{HPCS2002}, we have shown the very high power of asynchronism for parallel iterative algorithms in a global context of grid computing. In this article, we study the interest of coupling load balancing with asynchronism in such algorithms. After proposing a non-centralized version of dynamic load balancing which is best suited to asynchronism, we verify its efficiency by some experiments on a general Partial Differential Equation (PDE) problem. Finally, we give some general conditions for the use of load balancing to obtain good results with this kind of algorithms and discuss the choice of the residual as an efficient load estimator

Performance comparison of parallel programming environments for implementing AIAC algorithms

International audienceAIAC algorithms (Asynchronous Iterations Asynchronous Communications) are a particular class of parallel iterative algorithms. Their asynchronous nature makes them more efficient than their synchronous counterparts in numerous cases as has already been shown in previous works. The first goal of this article is to compare several parallel programming environments in order to see if there is one of them which is best suited to efficiently implement AIAC algorithms. The main criterion for this comparison consists in the performances achieved in a global context of grid computing for two classical scientific problems. Nevertheless, we also take into account two secondary criteria which are the ease of programming and the ease of deployment. The second goal of this study is to extract from this comparison the important features that a parallel programming environment must have in order to be suited for the implementation of AIAC algorithms

A decentralized convergence detection algorithm for asynchronous parallel iterative algorithms

We introduce a theoretical algorithm and its practical version to perform decentralized detection of the global convergence of parallel asynchronous iterative algorithms. We prove that even if the algorithm is completely decentralized, the detection of global convergence is achieved on one processor under the classical conditions. The proposed algorithm is very useful in the context of grid computing in which the processors are distributed and in which detecting the convergence on a master processor may be penalizing or even impossible as in Peer to Peer computations framework. Finally, the efficiency of the practical algorithm is illustrated in a typical experiment

NAPS: a Nomadic and Accurate Positioning System

International audienceProviding accurate pose estimation over time in various and unknown environments is a key component to the realization of autonomous missions where an absolute positioning system (like GPS) is not available. A lot of work has been done in that field where Simultaneous Localization And Mapping systems (SLAM) are still highly environment-dependent and computationally expensive. However, a major issue in SLAM solutions is the drift that appears in their odometry, due to error accumulation in the successive pose estimates. This may imply significant errors in the environment reconstruction or mission success. In this paper, a Nomadic and Accurate Positioning System (NAPS), based on the collaboration of mobile robots and their mutual sensing, is proposed. The originality and advantage of our solution is to provide a motion capture system with the nomadic ability. The result is environment-independent, computationally efficient, accurate and mobile. The system follows one or several mobile robots (explorers) and provides accurate positioning all along their mission. The NAPS is described as well as its moving process, and its accuracy is confirmed through a series of experiments, validating the whole approach

Jurisprudencia Civil Cubana, 02

Case law digest, v. 2. Civil law cases – Actos Propios (cont.)https://ecollections.law.fiu.edu/diaz-cruz-index/1064/thumbnail.jp

Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation

International audienceAccurate simulations of brain structures is a major problem in neuroscience. Many works are dedicated to design better models or to develop more efficient simulation schemes. In this paper, we propose a hybrid simulation scheme that combines time-stepping second-order integration of Hodgkin-Huxley (HH) type neurons with event-driven updating of the synaptic currents. As the HH model is a continuous model, there is no explicit spike events. Thus, in order to preserve the accuracy of the integration method, a spike detection algorithm is developed that accurately determines spike times. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently, memory consumption is significantly reduced while preserving execution time and accuracy of the simulations, especially the spike times of detailed point neuron models. The efficiency of the method, implemented in the SiReNe software, is demonstrated by the simulation of a striatum model which consists of more than 10⁶ neurons and 10⁸ synapses (each neuron has a fan-out of 504 post-synaptic neurons), under normal and Parkinson's conditions

A physiologically realistic computational model of the basal ganglia network

International audienceThe basal ganglia (BG) are a set of nuclei that process movement information: they refine and adjust simple movement actions. The BG has two major pathways: the striatum (STR)-indirect neuron pathway and the subthalamic (STN)-hyperdirect nucleus pathway. The GPe is the connecting nucleus between the two pathways. The STR inhibits the GPe and the STN excites the GPe which is divided into two types of neurons [1, 4], the prototypical and the arkypallidal. This discovery allows for a better understanding of the functioning of this neural network. We model the STN-GPeA-GPeP-STR(D2) network and study the influence of the nucleus on each other like in [2]. The neurons have been modeled as point neurons using the Hodgkin-Huxley formalism and the synapses as exponential functions. From extensive simulations performed with the SiReNe software (Neural network simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our network is in good agreement with the physiological results of [2]. This simulator is based on a hybrid method combining time-step and event-driven computations with a Runge-Kutta numerical method at inner level. GPe is mainly inhibited by GABAergic inputs ofthe STR and we study the impact of STR connectivity on GPe. We observe that the GPeP and GPeA react in opposite ways when the STR is activated, i.e. GPeP is entirely inhibited whereas the GPeA and STN are completely excited, as observed in [2]. This work aims at better understanding the synaptic connectivity scheme. This model will allow us to test hypotheses regarding the pathological rhythmogenesis in Parkinson disease, both at the cellular and connectivity levels