X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/gpc2011.git/blobdiff_plain/9d2bedb765159fa0135d334f703c85fb2384a859..70d6de89376491e1fefe8ef5a68591b9ce4b8773:/gpc2011.tex

diff --git a/gpc2011.tex b/gpc2011.tex
index eaf7101..c9f08f8 100644
--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -46,21 +46,21 @@
 \title{Gridification of a Radiotherapy Dose Computation Application with the XtremWeb-CH Environment}
 
 
-\author{Nabil Abdennhader\inst{1} \and Mohamed Ben Belgacem{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{2}}
+\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}
+\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 \\
-\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\\
-\email{marc.sauget@femtost.fr}
+\email{marc.sauget@univ-fcomte.fr}
 }
 
 
@@ -70,11 +70,12 @@ Laboratoire d'Informatique de l'universit\'{e}
   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 global (or volunteer) computing.
-  For this, we used the XtremWeb-CH environement. Experiments were
-  conducted on a real global computing testbed and show good speed-ups
-  and very acceptable platform overhead letting XtremWeb-CH a good candidate
-for deploying parallel applications over a global computing environment. 
+  we choose the architectural context of global (or volunteer)
+  computing.  For this, we used the XtremWeb-CH
+  environment. Experiments were conducted on a real global 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 global computing environment.
 \end{abstract}
 
 
@@ -94,14 +95,14 @@ 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. The main
 drawback of these methods is their computation times which can be
-rapidly be huge. In [] the authors proposed a novel approach, called
+rapidly huge. In \cite{NIMB2008} the authors proposed a novel 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{bcvsv08:ip} the authors proposed a parallel
-algorithm that enable to decompose the learning domain into
+consuming, in \cite{AES2009} the authors proposed a parallel
+algorithm that enables to decompose the learning domain into
 subdomains. The decomposition has the advantage to significantly
 reduce the complexity of the target functions to approximate.
 
@@ -113,15 +114,15 @@ 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
+approach presents 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
+we focus on the XtremWeb-CH environment\cite{}. We choose this environment
 because it tackles the centralized aspect of other global computing
-environments such as XtremWeb [] or Seti []. It tends to a
+environments such as XtremWeb\cite{} or Seti\cite{}. 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
@@ -130,7 +131,7 @@ 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
+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
@@ -145,29 +146,27 @@ end in Section 6 by some concluding remarks and perspectives.
   \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.
-
-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. 
+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~\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
@@ -186,23 +185,21 @@ result must be obtain in a homogeneous environment.
 %   \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 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
+The secondary stage  of the {\it{Neurad}} project is the  learning step and this
+is  the most time  consuming step.  This step  is performed  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.
 
 
@@ -210,26 +207,28 @@ response for the global domain of study.
   \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}
 
-
-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
-(Qu'en est-il pour nos test ?).
-
+% j'ai relu mais pas vu le probleme 
+ 
+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~\cite{AES2009}.
+
+%(Qu'en est-il pour nos test ?).
+% Ce paramètre a deja été etudié dans un précédent papier, il a donc choisi d'être fixe
+% pour ces tests-ci.
 
 
 \section{The XtremWeb-CH environment}
@@ -252,27 +251,27 @@ The second step of the application, and the most time consuming, is
 the learning itself. This is the one which has been parallelized,
 using the XWCH environment. As exposed in the section 2, the
 parallelization relies on a partitionning of the global
-dataset. Following this partitionning all learning tasks execute in
-parallel independently with their own local data part, with no
-communication, following the fork-join model. Clearly, this
+dataset. Following this partitionning all learning tasks are executed
+in parallel independently 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.
 
 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
+\item We first send the learning application and its data to the
   middleware (more precisely on warehouses (DW)) and create the
-  computation module,
-\item when a worker (W) is ready to compute, it requests a task to
-  execute to the coordinator (Coord.),
+  computation module;
+\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 and so can start the computation. 
-\item At the end of the learning process, the worker sends the result,, to a warehouse.
+\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.
+process. This latter step is out of the scope of this paper.
 
 
 \begin{figure}[ht]
@@ -285,7 +284,7 @@ process.
 \section{Experimental results}
 \label{sec:neurad_xp}
 
-The aim of this section is to describe and analyse the experimental
+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 global computing testbed.
@@ -294,17 +293,17 @@ data and on a real global computing testbed.
 \label{sec:neurad_cond}
 
 The size of the input data is about 2.4Gb. In order to avoid that data
-noise appears and disturb the learning process, these data can be
-divided into 25 part, at most. This generates input data parts of
+noise appears and disturbs 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. Unfortunately, the data decomposition limitation does not allow
 us to use more than 25 computers (XWCH workers). Nevertheless, we used two
-distincts deployments of XWCH:
+distinct deployments of XWCH:
 \begin{enumerate} 
 
-\item In the first one, called ``ditributed XWCH'' in the following,
-  the XWCH coordinator and the warehouses were situated in Geneva,
+\item In the first one, called ``distributed XWCH'' in the following,
+  the XWCH coordinator and the warehouses were located in Geneva,
   Switzerland while the workers were running in the same local cluster
   in Belfort, France.
 
@@ -313,7 +312,7 @@ distincts deployments of XWCH:
   the same local cluster.  
 
 \end{enumerate}
-For the both deployments, during the day these machines were used by
+For both deployments, during the day these machines were used by
 students of the Computer Science Department of the IUT of Belfort.
 
 In order to evaluate the overhead induced by the use of the platform
@@ -324,7 +323,7 @@ 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 $1e^{-2}$.
+$0.50e^{-1}$, $0.25e^{-1}$, and $1e^{-2}$.
 
 
 \subsubsection{Results}
@@ -332,17 +331,19 @@ $0.50e^{-1}$, $0.25e^{-1}$ and $1e^{-2}$.
 
 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 measure
-of the same step for both kind of execution i.e. sending of local data and the
-executable, the learning process, and retrieving the results. The
-results represent the average time of $x$ executions.
+deployment) and without XWCH. These results correspond to the measures
+of the same steps for both kinds of execution, i.e. sending of local
+data and the executable, the learning process, and retrieving the
+results. Results represent the average time of $?? x ??$ 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\\
+    ~Precision~ & ~1 machine~ & ~Without XWCH~ & ~With XWCH~ & ~With
+    local XWCH~ \\
     \hline
      $1e^{-1}$ & 5190 & 558 & 759 & 629\\
     $0.75e^{-1}$ & 6307 & 792 & 1298 & 801 \\
@@ -351,6 +352,7 @@ results represent the average time of $x$ executions.
     $1e^{-2}$ & 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}
@@ -381,12 +383,12 @@ though the same steps were done, 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 executions without XWCH, all the machines
+bandwidth. In addition, in 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 underline that, unsurprisingly, deploying a local
+This underlines that, unsurprisingly, deploying a local
 coordinator and one or more warehouses near a cluster of workers can
 enhance computations and platform performances. 
 
@@ -396,19 +398,26 @@ enhance computations and platform performances.
 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 applications
-present a very time consuming step i.e. the learning step. Due to the
+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 choose to parallelize it
 using the XtremWeb-CH global 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 global computing environment.
 
-Our future works, include the testing of the application on a more
+Our future works include the testing of the application on a more
 large 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
-(demander ici des précisions à Marc).
+
+%(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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 \bibliography{biblio}