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+\begin{document}
+
+\title{Gridification of a Radiotherapy Dose Computation Application with the XtremWeb-CH Environment}
+
+\author{Nabil Abdennhader\inst{1} \and Raphaël Couturier\inst{1} \and David \and
+ Julien Henriet\inst{2} \and Laiymani\inst{1} \and Sébastien Miquée\inst{1}
+ \and Marc Sauget\inst{2}}
+
+\institute{Laboratoire d'Informatique de l'universit\'{e}
+ de Franche-Comt\'{e} \\
+ IUT Belfort-Montbéliard, Rue Engel Gros, 90016 Belfort - France \\
+\email{raphael.couturier, david.laiymani, sebastien.miquee@univ-fcomte.fr}
+\and
+ FEMTO-ST, ENISYS/IRMA, F-25210 Montb\'{e}liard , FRANCE\\
+}
+%\email{\texttt{[laiymani]@lifc.univ-fcomte.fr}}}
+
+
+\maketitle
+
+\begin{abstract}
+
+\end{abstract}
+
+%-------------INTRODUCTION--------------------
+\section{Introduction}
+
+The use of distributed architectures for solving large scientific problems seems
+to become mandatory in a lot of cases. For example, in the domain of
+radiotherapy dose computation the problem is crucial. The main goal of external
+beam radiotherapy is the treatment of tumours while minimizing exposure to
+healthy tissue. Dosimetric planning has to be carried out in order to optimize
+the dose distribution within the patient is necessary. Thus, for determining the
+most accurate dose distribution during treatment planning, a compromise must be
+found between the precision and the speed of calculation. Current techniques,
+using analytic methods, models and databases, are rapid but lack
+precision. Enhanced precision can be achieved by using calculation codes based,
+for example, on Monte Carlo methods. In [] the authors proposed a novel approach
+based on the use of neural networks. The approach is based on the collaboration
+of computation codes and multi-layer neural networks used as universal
+approximators. It provides a fast and accurate evaluation of radiation doses in
+any given environment for given irradiation parameters. As the learning step is
+often very time consumming, in \cite{bcvsv08:ip} the authors proposed a parallel
+algorithm that enable to decompose the learning domain into subdomains. The
+decomposition has the advantage to significantly reduce the complexity of the
+target functions to approximate.
+
+Now, as there exist several classes of distributed/parallel architectures
+(supercomputers, clusters, global computing...) we have to choose the best
+suited one for the parallel Neurad application. The Global or Volunteer
+computing model seems to be an interesting approach. Here, the computing power
+is obtained by agregating unused (or volunteer) public resources connected to
+the Internet. For our case, we can imagine for example, that a part of the
+architecture will be composed of some of the different computers of the
+hospital. This approach present the advantage to be clearly cheaper than a more
+dedicated approach like the use of supercomputer or clusters.
+
+The aim of this paper is to propose and evaluate a gridification of the Neurad
+application (more precisely, of the most time consuming part, the learning step)
+using a Global computing approach. For this, we focus on the XtremWeb-CH
+environnement []. We choose this environnent because it tackles the centralized
+aspect of other global computing environments such as XTremWeb [] or Seti []. It
+tends to a peer-to-peer approach by distributing some components of the
+architecture. For instance, the computing nodes are allowed to directly
+communicate. Experimentations were conducted on a real Global Computing
+testbed. The results are very encouraging. They exhibit an interesting speed-up
+and show that the overhead induced by the use of XTremWeb-CH is very acceptable.
+
+The paper is organized as follows. In section 2 we present the Neurad
+application and particularly it most time consuming part i.e. the learning
+step. Section 3 details the XtremWeb-CH environnement while in section 4 we
+expose the gridification of the Neurad application. Experimental results are
+presented in section 5 and we end in section 6 by some concluding remarks and
+perspectives.
+
+\section{The Neurad application}
+
+\begin{figure}[http]
+ \centering
+ \includegraphics[width=0.7\columnwidth]{figures/neurad.pdf}
+ \caption{The Neurad projects}
+ \label{f_neurad}
+\end{figure}
+
+The \emph{Neurad}~\cite{Neurad} project presented in this paper takes place in a
+multi-disciplinary project , involving medical physicists and computer
+scientists whose goal is to enhance the treatment planning of cancerous tumors
+by external radiotherapy. In our previous
+works~\cite{RADIO09,ICANN10,NIMB2008}, we have proposed an original approach to
+solve scientific problems whose accurate modeling and/or analytical description
+are difficult. That method is based on the collaboration of computational codes
+and neural networks used as universal interpolator. Thanks to that method, the
+\emph{Neurad} software provides a fast and accurate evaluation of radiation
+doses in any given environment (possibly inhomogeneous) for given irradiation
+parameters. We have shown in a previous work (\cite{AES2009}) the interest to
+use a distributed algorithm for the neural network learning. We use a classical
+RPROP algorithm with a HPU topology to do the training of our neural network.
+
+The Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts are
+clearly independant : the initial data production, the learning process and the
+dose deposit evaluation. The first step, the data production, is outside the
+{\it{Neurad}} project. They are many solutions to obtains data about the
+radiotherapy treatments like the measure or the simulation. The only essential
+criterion is that the result must be obtain in a homogeneous environment. We
+have chosen to use only a Monte Carlo simulation because this tools are the
+references in the radiotherapy domains. The advantages to use data obtain with a
+Monte Carlo simulator are the following : accuracy, profusing, quantify error
+and regularity of measure point. But, they are too disagreement and the most
+important is the statistical noise forcing a data post treatment. The
+Figure~\ref{f_tray} present the general behavior of a dose deposit in water.
+
+
+\begin{figure}[http]
+ \centering
+ \includegraphics[width=0.7\columnwidth]{figures/testC.pdf}
+ \caption{Dose deposit by a photon beam of 24 mm of width in water (Normalized value). }
+ \label{f_tray}
+\end{figure}
+
+The secondary stage of the {\it{Neurad}} project is about the learning step and
+it is the most time consuming step. This step is off-line but is it important to
+reduce the time used for the learning process to keep a workable tools. Indeed,
+if the learning time is too important (for the moment, this time could reach one
+week for a limited works domain), the use of this process could be be limited
+only at a major modification of the use context. However, it is interesting to
+do an update to the learning process when the bound of the learning domain
+evolves (evolution in material used for the prosthesis or evolution on the beam
+(size, shape or energy)). The learning time is linked with the volume of data
+who could be very important in real medical context. We have work to reduce
+this learning time with a parallel method of the learning process using a
+partitioning method of the global dataset. The goal of this method is to train
+many neural networks on sub-domain of the global dataset. After this training,
+the use of this neural networks together allows to obtain a response for the
+global domain of study.
+
+
+\begin{figure}[h]
+ \centering
+ \includegraphics[width=0.5\columnwidth]{figures/overlap.pdf}
+ \caption{Overlapping for a sub-network in a two-dimensional domain with ratio
+ $\alpha$.}
+ \label{fig:overlap}
+\end{figure}
+
+
+However, performing the learnings on sub-domains constituting a partition of the
+initial domain is not satisfying according to the quality of the results. This
+comes from the fact that the accuracy of the approximation performed by a neural
+network is not constant over the learned domain. Thus, it is necessary to use
+an overlapping of the sub-domains. The overall principle is depicted in
+Figure~\ref{fig:overlap}. In this way, each sub-network has an exploitation
+domain smaller than its training domain and the differences observed at the
+borders are no longer relevant. Nonetheless, in order to preserve the
+performances of the parallel algorithm, it is important to carefully set the
+overlapping ratio $\alpha$. It must be large enough to avoid the border's
+errors, and as small as possible to limit the size increase of the data subsets.
+
+
+
+
+
+\section{The XtremWeb-CH environment}
+\section{Neurad gridification with XTremweb-ch}
+\section{Experimental results}
+\section{Conclusion and future works}
+
+
+
+\bibliographystyle{plain}
+\bibliography{biblio}
+
+
+
+\end{document}
