X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/gpc2011.git/blobdiff_plain/327afc9fede0d1000ae5402bb80deaa057adeb7c..a1415d6476d377efc33ae06dac9c949ee467e0ee:/gpc2011.tex

diff --git a/gpc2011.tex b/gpc2011.tex
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--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -68,113 +68,126 @@
 %-------------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]
@@ -186,24 +199,25 @@ global domain of study.
 \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}
 
