Section 4 correction.

[gpc2011.git] / gpc2011.tex

diff --git a/gpc2011.tex b/gpc2011.tex
index 027142a..b9f31ac 100644 (file)
--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -45,169 +45,392 @@
 
 \title{Gridification of a Radiotherapy Dose Computation Application with the XtremWeb-CH Environment}
 
 
 \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}
+\author{Nabil Abdennhader\inst{1} \and Mohamed Ben Belgacem\inst{1} \and Raphaël Couturier\inst{2} \and
+  David Laiymani\inst{2} \and Sébastien  Miquée\inst{2} \and Marko Niinimaki\inst{1} \and Marc Sauget\inst{3}}
+
+\institute{
+University of Applied Sciences Western Switzerland, hepia Geneva,
+Switzerland \\
+\email{nabil.abdennadher@hesge.ch,mohamed.benbelgacem@unige.ch,markopekka.niinimaeki@hesge.ch}
+\and
+Laboratoire d'Informatique de l'universit\'{e}

   de Franche-Comt\'{e} \\
   IUT Belfort-Montbéliard, Rue Engel Gros, 90016 Belfort - France \\
   de Franche-Comt\'{e} \\
   IUT Belfort-Montbéliard, Rue Engel Gros, 90016 Belfort - France \\

-\email{raphael.couturier, david.laiymani, sebastien.miquee@univ-fcomte.fr}
+\email{\{raphael.couturier,david.laiymani,sebastien.miquee\}@univ-fcomte.fr}

 \and
  FEMTO-ST, ENISYS/IRMA, F-25210 Montb\'{e}liard , FRANCE\\
 \and
  FEMTO-ST, ENISYS/IRMA, F-25210 Montb\'{e}liard , FRANCE\\

+\email{marc.sauget@univ-fcomte.fr}

 }
 }

-%\email{\texttt{[laiymani]@lifc.univ-fcomte.fr}}}

 
 
 \maketitle
 
 \begin{abstract} 
 
 
 \maketitle
 
 \begin{abstract} 

-  
+  This paper presents the design and the evaluation of the
+  gridification of a radiotherapy dose computation application. Due to
+  the inherent characteristics of the application and its execution,
+  we choose the architectural context of  volunteer
+  computing.  For this, we used the XtremWeb-CH
+  environment. Experiments were conducted on a real volunteer computing
+  testbed and show good speed-ups and very acceptable platform
+  overhead, letting XtremWeb-CH be a good candidate for deploying
+  parallel applications over a volunteer computing environment.

 \end{abstract}
 
 \end{abstract}
 

+

 %-------------INTRODUCTION--------------------
 \section{Introduction}
 
 %-------------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. 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 the Monte Carlo methods. The main
+drawback of these methods is their computation times which can 
+rapidly become huge. In \cite{NIMB2008} the authors proposed a new approach, called
+Neurad, 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{AES2009} the authors proposed a parallel
+algorithm that enables to decompose the learning domain into
+subdomains. The decomposition has the advantage of significantly
+reducing the complexity of the target functions to approximate.
+
+Now, as there exist several classes of distributed/parallel
+architectures (supercomputers, clusters, global computing\dots{}) we
+have to choose the best suited one for the parallel Neurad
+application. The volunteer (or global) 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. In 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 presents the advantage of being clearly
+cheaper than a more dedicated approach like the use of supercomputers
+or clusters. Furthermore and as we will see in the remainder, the
+studied parallel algorithm corresponds very well to this computation model.
+
+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 volunteer computing approach. For
+this, we focus on the XtremWeb-CH environment\cite{xwch}. We chose
+this environment because it tackles the centralized aspect of other
+global computing environments such as XtremWeb\cite{xtremweb} or
+Seti\cite{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 its most time consuming part, i.e. the
+learning step. Section 3 details the XtremWeb-CH environment and
+Section 4 exposes 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}
 
 \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
   \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
+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  solving  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 of using a distributed algorithm for the neural
+network learning. We  use a classical RPROP~\footnote{Resilient backpropagation}
+algorithm with a  HPU~\footnote{High order processing units} 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  of 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 obtained in an 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).}
+%   \label{f_tray}
+% \end{figure}
+
+The secondary stage  of the {\it{Neurad}} project is the  learning step and this
+is  the most time  consuming step.  This step  is performed  offline 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
 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.
+(size, shape or energy)). The learning time is related to the volume of data which could be  very important in  a real  medical context.  Some 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]
   \centering
   \includegraphics[width=0.5\columnwidth]{figures/overlap.pdf}
   \caption{Overlapping for a sub-network  in a two-dimensional domain with ratio
 
 
 \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

-    $\alpha$.}
+    $\alpha$}

   \label{fig:overlap}
 \end{figure}
 
   \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
+% j'ai relu mais pas vu le probleme 
+ 
+However, performing the learning on  sub-domains constituting a partition of the
+initial domain may  not be satisfying depending on  the chosen quality  of the results. This

 comes from the fact that the accuracy of the approximation performed by a neural
 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
+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
 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.
-
-
+performance  of the parallel  algorithm, it  is important  to carefully  set the
+overlapping  ratio $\alpha$.  It  must both be  large  enough to  avoid the  border's
+errors,  and as  small  as  possible to  limit  the size  increase  of the  data
+subsets~\cite{AES2009}.

 
 
 
 \section{The XtremWeb-CH environment}
 
 
 
 \section{The XtremWeb-CH environment}

-\section{The XtremWeb-CH environment (\textit{XWCH})}

 \input{xwch.tex}
 \input{xwch.tex}

+
+\section{The Neurad gridification}
+
+\label{sec:neurad_gridif}
+
+
+As previously exposed, the Neurad application can be divided into
+three steps.  The goal of the first step is to decompose the data
+representing the dose distribution on an area. This area contains
+various parameters, like the nature of the medium and its
+density. This part is out of the scope of this paper.
+%Multiple ``views'' can be
+%superposed in order to obtain a more accurate learning. 
+
+The second step of the application, and the most time consuming, is the learning
+in  itself.  This  is the  one  which  has  been  parallelized, using  the  XWCH
+environment.  As  exposed  in  section   2,  the  parallelization  relies  on  a
+partitioning  of the global  dataset. Following  this partitioning  all learning
+tasks are  independently executed  in parallel with  their own local  data part,
+with no communication, following  the fork/join model. Clearly, this computation
+fits well with the model of the chosen middleware.
+
+\begin{figure}[ht]
+  \centering
+  \includegraphics[width=8cm]{figures/neurad_gridif}
+  \caption{The proposed Neurad gridification}
+  \label{fig:neurad_grid}
+\end{figure}
+
+
+The execution scheme is then the following (see Figure
+\ref{fig:neurad_grid}):
+\begin{enumerate}
+\item We first send the learning application and its data to the
+  middleware. In a first time, we send the application to data
+  warehouses (DW), and the create an "application module" on the
+  coordinator (Coord.) including references retrieved from the
+  previous sending operation. In a second time, we apply the same
+  process to application data.
+\item When a worker (W) is ready to compute, it requests a task to
+  execute to the coordinator (Coord.);
+\item The coordinator assigns the worker a task. This last one
+  retrieves the application and its assigned data, by requesting them
+  to DW with references sent by the coordinator, and so can start the
+  computation;
+\item At the end of the learning process, the worker sends the result
+  to a warehouse.
+\end{enumerate}
+
+The last step of the application is to retrieve these results (some
+weighted neural networks) and exploit them through a dose distribution
+process. This last step is out of the scope of this paper.
+
+
+

 \section{Experimental results}
 \section{Experimental results}

+\label{sec:neurad_xp}
+
+The aim of this section is to describe and analyze the experimental
+results we have obtained with the parallel Neurad version previously
+described. Our goal was to carry out this application with real input
+data and on a real volunteer computing testbed.
+
+\subsubsection{Experimental conditions}
+\label{sec:neurad_cond}
+
+The size of the input data is about 2.4Gb. In order to avoid 
+noises to appear and disturb the learning process, these data can be
+divided into, at most, 25 parts. This generates input data parts of
+about 15Mb (in a compressed format). The output data, which are
+retrieved after the process, are about 30Kb for each part. We used two
+distinct deployments of XWCH:
+\begin{enumerate} 
+
+\item In the first one, called ``distributed XWCH'',
+  the XWCH coordinator and the warehouses were located in Geneva,
+  Switzerland while the workers were running in the same local cluster
+  in Belfort, France.
+
+\item The second deployment, called ``local XWCH'' is a local
+  deployment where  coordinator, warehouses and workers were, in
+  the same local cluster, at the same time.  
+
+\end{enumerate}
+For both deployments, the local cluster is a campus cluster and during
+the day these machines were used by students of the Computer Science
+Department of the IUT of Belfort.  Unfortunately, the data
+decomposition limitation does not allow us to use more than 25
+computers (XWCH workers).
+
+In order  to evaluate the overhead  induced by the  use of the platform  we have
+furthermore compared  the execution of  the Neurad application with  and without
+the XWCH platform. For  the latter case, we want to insist  on the fact that the
+testbed consists only in workers deployed  with their respective data by the use
+of shell  scripts. No specific middleware was  used and the workers  were in the
+same local cluster.
+
+Finally, five computation precisions were used: $1e^{-1}$, $0.75e^{-1}$,
+$0.50e^{-1}$, $0.25e^{-1}$, and $1e^{-2}$.
+
+
+\subsubsection{Results}
+\label{sec:neurad_result}
+
+
+Table \ref{tab:neurad_res} presents the execution times of the Neurad
+application on 25 machines with XWCH (local and distributed
+deployment) and without XWCH. These results correspond to the measures
+of the same steps for both kinds of execution, i.e. the sending of local
+data and the executable, the learning process, and retrieving the
+results. Results represent the average time of $5$ executions.
+
+
+\begin{table}[h!]
+  \renewcommand{\arraystretch}{1.7}
+  \centering
+  \begin{tabular}[h!]{|c|c|c|c|c|}
+    \hline
+    ~Precision~ & ~1 machine~ & ~Without XWCH~ & ~With XWCH~ & ~With
+    local XWCH~ \\
+    \hline
+     $1e^{-1}$ & 5190 & 558 & 759 & 629\\
+    $0.75e^{-1}$ & 6307 & 792 & 1298 & 801 \\
+    $0.50e^{-1}$ & 7487 & 792 & 1010 & 844 \\
+    $0.25e^{-1}$ & 7787 & 791 & 1000 & 852\\
+    $1e^{-2}$ & 11030 & 1035 & 1447 & 1108 \\
+    \hline
+  \end{tabular}
+  \vspace{0.3cm}
+\caption{Execution time in seconds of the Neurad application, with and without using the XWCH platform}
+  \label{tab:neurad_res}
+\end{table}
+
+%\begin{table}[ht]
+%  \centering
+%  \begin{tabular}[h]{|c|c|c|}
+%    \hline
+%    Precision & Without XWCH & With XWCH \\
+%    \hline
+%    $1e^{-1}$ & $558$s & $759$s\\
+%    \hline
+%  \end{tabular}
+%  \caption{Execution time in seconds of Neurad application, with and without using XtremWeb-CH platform}
+%  \label{tab:neurad_res}
+%\end{table}
+
+
+As we can see, in the case of a local deployment the overhead induced
+by the use of the XWCH platform is about $7\%$. It is clearly a low
+overhead. Now, for the distributed deployment, the overhead is about
+$34\%$. Regarding  the benefits of the platform, it is a very
+acceptable overhead which can be explained by the following points.
+
+First, we point out that the conditions of executions are not really
+identical between, with and without, XWCH contexts. For this last one,
+though the same steps were achieved out, all transfer processes are inside a
+local cluster with a high bandwidth and a low latency. Whereas when
+using XWCH, all transfer processes (between datawarehouses, workers,
+and the coordinator) used a wide network area with a smaller
+bandwidth. In addition, in the executions without XWCH, all the machines
+started immediately the computation, whereas when using the XWCH
+platform, a latency is introduced by the fact that a computation
+starts on a machine, only when this one requests a task.
+
+This underlines that, unsurprisingly, deploying a local
+coordinator and one or more warehouses near a cluster of workers can
+enhance computations and platform performances. 
+
+
+
+

 \section{Conclusion and future works}
 
 \section{Conclusion and future works}
 

+In this paper, we have presented a gridification of a real medical
+application, the Neurad application. This radiotherapy application
+tries to optimize the irradiated dose distribution within a
+patient. Based on a multi-layer neural network, this application
+presents a very time consuming step, i.e. the learning step. Due to the
+computing characteristics of this step, we have chosen to parallelize it
+using the XtremWeb-CH volunteer computing environment. Obtained
+experimental results show good speed-ups and underline that overheads
+induced by XWCH are very acceptable, letting it be a good candidate
+for deploying parallel applications over a volunteer computing environment.
+
+Our future works include the testing of the application on a 
+larger scale testbed. This implies, the choice of a data input set
+allowing a finer decomposition. Unfortunately, this choice of input
+data is not trivial and relies on a large number of parameters.
+
+We are also planning to test XWCH with parallel applications where
+communication between workers occurs during the execution. In this
+way, the use of the asynchronous iteration model \cite{bcl08} may be
+an interesting perspective.
+
+%(demander ici des précisions à Marc).
+% Si tu veux parler de l'ensembles des paramètres que l'on peut utiliser pour caractériser les conditions d'irradiations
+% tu peux parler : 
+% - caracteristiques du faisceaux d'irradiation (beam size (de quelques mm à plus de 40 cm),  energy, SSD (source surface distance), 
+% - caractéritiques de  la matière : density 
+

 
 
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