X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/gpc2011.git/blobdiff_plain/327afc9fede0d1000ae5402bb80deaa057adeb7c..26c95d1f116e4dcc7ddc64b2f7717d90cf23f960:/gpc2011.tex

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--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -45,168 +45,392 @@
 
 \title{Gridification of a Radiotherapy Dose Computation Application with the XtremWeb-CH Environment}
 
-\author{Nabil Abdennhader\inst{1} \and RaphaÃ«l Couturier\inst{1} \and David \and
-  Julien  Henriet\inst{2} \and  Laiymani\inst{1}  \and SÃ©bastien  MiquÃ©e\inst{1}
-  \and Marc Sauget\inst{2}}
 
-\institute{Laboratoire d'Informatique de l'universit\'{e}
+\author{Nabil Abdennadher\inst{1} \and Mohamed Ben Belgacem\inst{1} \and RaphaÃ«l Couturier\inst{2} \and
+  David Laiymani\inst{2} \and SÃ©bastien  MiquÃ©e\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-MontbÃ©liard, 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@univ-fcomte.fr}
 }
-%\email{\texttt{[laiymani]@lifc.univ-fcomte.fr}}}
 
 
 \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}
 
+
 %-------------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}
-  \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.
-
-
-\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
-(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
-    $\alpha$.}
+    $\alpha$}
   \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
+% 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
-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
-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}
+\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{The XtremWeb-CH environment}
-\section{Neurad gridification with XTremweb-ch}
 \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}
 
+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 prÃ©cisions Ã  Marc).
+% Si tu veux parler de l'ensembles des paramÃ¨tres que l'on peut utiliser pour caractÃ©riser les conditions d'irradiations
+% tu peux parler : 
+% - caracteristiques du faisceaux d'irradiation (beam size (de quelques mm Ã  plus de 40 cm),  energy, SSD (source surface distance), 
+% - caractÃ©ritiques de  la matiÃ¨re : density 
+
 
 
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