correction ortho nabil

[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 Abdennadher\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}
\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@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 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 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  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 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
    $\alpha$}
  \label{fig:overlap}
\end{figure}

% 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
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 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{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 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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