X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/gpc2011.git/blobdiff_plain/a19006a5bab5c146c8e8841c24dc7ba9a77da00d..fd2636199b12d44a403d787edfa93c2c6f45ae1d:/gpc2011.tex

diff --git a/gpc2011.tex b/gpc2011.tex
index 8c7d299..b9f31ac 100644
--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -265,18 +265,24 @@ 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;
+  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 and so can start the computation;
-\item At the end of the learning process, the worker sends the result to a warehouse.
+\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.
+process. This last step is out of the scope of this paper.