\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}
}
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}
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.
(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
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
\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
% \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.
\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}
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]
\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.
\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.
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
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}
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 \\
$1e^{-2}$ & 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}
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.
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
+
+
\bibliographystyle{plain}
\bibliography{biblio}
