1er draft complet. A relire of course. Manque la biblio

[gpc2011.git] / gpc2011.tex

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\begin{document}

\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}}

\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 \\
\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}
}


\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 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. 
\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 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 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
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
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 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 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.

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).}
%   \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
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
    $\alpha$.}
  \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 ?).



\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 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
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
  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.),
\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.
\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.


\begin{figure}[ht]
  \centering
  \includegraphics[width=8cm]{figures/neurad_gridif}
  \caption{The proposed Neurad gridification}
  \label{fig:neurad_grid}
\end{figure}

\section{Experimental results}
\label{sec:neurad_xp}

The aim of this section is to describe and analyse 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.

\subsubsection{Experimental conditions}
\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
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:
\begin{enumerate} 

\item In the first one, called ``ditributed XWCH'' in the following,
  the XWCH coordinator and the warehouses were situated 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 both coordinator, warehouses and workers were in
  the same local cluster.  

\end{enumerate}
For the 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
we have furthermore compared the execution of the Neurad application
with and without the XWCH platform. For the latter case, we mean 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 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.


\begin{table}[h!]
  \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}
\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 to 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 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
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
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 applications
present 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
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).

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