%-------------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.
+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 tumors 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, to determine 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 using neural
+networks. This 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 consuming, 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 aggregating 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 supercomputers 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 environment []. We choose this environment
+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. Experiments 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 environment 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}
+ \caption{The Neurad project}
\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.
+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.
+
+Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts
+are clearly independent: 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 obtain 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 kind of tool is the
+reference in the radiotherapy domains. The advantages to use data
+obtained with a Monte Carlo simulator are the following: accuracy,
+profusion, quantified error and regularity of measure points. But,
+there exist also some disagreements and the most important is the
+statistical noise, forcing a data post treatment. Figure~\ref{f_tray}
+presents 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). }
+ \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.
+The secondary stage of the {\it{Neurad}} project is the learning step
+and this is the most time consuming step. This step is off-line but it
+is important to reduce the time used for the learning process to keep
+a workable tool. Indeed, if the learning time is too huge (for the
+moment, this time could reach one week for a limited domain), this
+process should not be launched at any time, but only when a major
+modification occurs in the environment, like a change of context for
+instance. However, it is interesting to update the knowledge of the
+neural network, by using the learning process, when the domain evolves
+(evolution in material used for the prosthesis or evolution on the
+beam (size, shape or energy)). The learning time is related to the
+volume of data who could be very important in a real medical context.
+A work has been done to reduce this learning time with the
+parallelization of the learning process by using a partitioning method
+of the global dataset. The goal of this method is to train many neural
+networks on sub-domains of the global dataset. After this training,
+the use of these neural networks all together allows to obtain a
+response for the global domain of study.
\begin{figure}[h]
\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.
-
-
+However, performing the learning 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 performance 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}
+\input{xwch.tex}
+
\section{Experimental results}
\section{Conclusion and future works}
