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)
+ we choose the architectural context of volunteer
computing. For this, we used the XtremWeb-CH
- environment. Experiments were conducted on a real global computing
+ 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 global computing environment.
+ overhead, letting XtremWeb-CH be a good candidate for deploying
+ parallel applications over a volunteer computing environment.
\end{abstract}
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
+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. 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 presents the advantage to be clearly cheaper than a more
-dedicated approach like the use of supercomputers or clusters.
+dedicated approach like the use of supercomputers or
+clusters. Furthermore and as we will see in the remainder, the studied
+parallel algorithm fits well 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 Global Computing approach. For this,
+part, the learning step) using a volunteer computing approach. For this,
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\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
-Computing testbed. The results are very encouraging. They exhibit an
+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 last step of the application is to retrieve these results (some
weighted neural networks) and exploit them through a dose distribution
-process. This latter step is out of the scope of this paper.
+process.
\begin{figure}[ht]
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.
+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 that data
+The size of the input data is about 2.4Gb. In order to avoid that
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
+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'' in the following,
+\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.
the same local cluster.
\end{enumerate}
-For both deployments, during the day these machines were used by
-students of the Computer Science Department of the IUT of Belfort.
+For both deployments, le 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
\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. sending of local
data and the executable, the learning process, and retrieving the
-results. Results represent the average time of $?? x ??$ executions.
+results. Results represent the average time of $5$ executions.
\begin{table}[h!]
enhance computations and platform performances.
+
+
\section{Conclusion and future works}
In this paper, we have presented a gridification of a real medical
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
+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 global computing environment.
+for deploying parallel applications over a volunteer 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
+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
