X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/1d9e9316f105c040c1a777d2e16d9002b041e9c4..eee5d96ed771994c95b62ea44cf071ed66dde1a8:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index 70f5e86..a4d8085 100644
--- a/paper.tex
+++ b/paper.tex
@@ -287,10 +287,10 @@ The algorithm in Figure~\ref{alg:02} includes the procedure of the residual mini
 \subsection{Simulation of two-stage methods using SimGrid framework}
 \label{sec:04.02}
 
-One of our objectives when simulating the application in SIMGRID is, as in real life, to get accurate results (solutions of the problem) but also ensure the test reproducibility under the same conditions. According our experience, very few modifications are required to adapt a MPI program to run in SIMGRID simulator using SMPI (Simulator MPI).The first modification is to include SMPI libraries and related header files (smpi.h). The second and important modification is to eliminate all global variables in moving them to local subroutine or using a Simgrid selector called "runtime automatic switching" (smpi/privatize\_global\_variables). Indeed, global variables can generate side effects on runtime between the threads running in the same process, generated by the Simgrid to simulate the grid environment.The last modification on the MPI program pointed out for some cases, the review of the sequence of the MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions which might cause an infinite loop.
+One of our objectives when simulating the application in Simgrid is, as in real life, to get accurate results (solutions of the problem) but also ensure the test reproducibility under the same conditions. According our experience, very few modifications are required to adapt a MPI program to run in Simgrid simulator using SMPI (Simulator MPI).The first modification is to include SMPI libraries and related header files (smpi.h). The second and important modification is to eliminate all global variables in moving them to local subroutine or using a Simgrid selector called "runtime automatic switching" (smpi/privatize\_global\_variables). Indeed, global variables can generate side effects on runtime between the threads running in the same process, generated by the Simgrid to simulate the grid environment.The last modification on the MPI program pointed out for some cases, the review of the sequence of the MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions which might cause an infinite loop.
 
 
-\paragraph{SIMGRID Simulator parameters}
+\paragraph{Simgrid Simulator parameters}
 
 \begin{itemize}
 	\item hostfile: Hosts description file.
@@ -332,8 +332,8 @@ have been chosen for the study in this paper. \\
 
 \textbf{Step 2} : Collect the software materials needed for the
 experimentation. In our case, we have two variants algorithms for the
-resolution of three 3D-Poisson problem: (1) using the classical GMRES (Algo-1)(2) and the multisplitting method (Algo-2). In addition, SIMGRID simulator has been chosen to simulate the behaviors of the
-distributed applications. SIMGRID is running on the Mesocentre datacenter in Franche-Comte University but also in a virtual machine on a laptop. \\
+resolution of three 3D-Poisson problem: (1) using the classical GMRES (Algo-1)(2) and the multisplitting method (Algo-2). In addition, Simgrid simulator has been chosen to simulate the behaviors of the
+distributed applications. Simgrid is running on the Mesocentre datacenter in Franche-Comte University but also in a virtual machine on a laptop. \\
 
 \textbf{Step 3} : Fix the criteria which will be used for the future
 results comparison and analysis. In the scope of this study, we retain
@@ -400,7 +400,7 @@ synchronous mode}
 In the scope of this paper, our first objective is to demonstrate the
 Algo-2 (Multisplitting method) shows a better performance in grid
 architecture compared with Algo-1 (Classical GMRES) both running in
-\textbf{\textit{synchronous mode}}. Better algorithm performance
+\textit{synchronous mode}. Better algorithm performance
 should means a less number of iterations output and a less execution time
 before reaching the convergence. For a systematic study, the experiments
 should figure out that, for various grid parameters values, the
@@ -611,12 +611,11 @@ Table 6 : CPU Power impact \\
 %\label{overflow}}
 \end{figure}
 
-Using the SIMGRID simulator flexibility, we have tried to determine the
+Using the Simgrid simulator flexibility, we have tried to determine the
 impact on the algorithms performance in varying the CPU power of the
 clusters nodes from 1 to 19 GFlops. The outputs depicted in the figure 6
 confirm the performance gain, around 95\% for both of the two methods,
-after adding more powerful CPU. Note that the execution time axis in the
-figure is in logarithmic scale.
+after adding more powerful CPU. 
 
 \subsection{Comparing GMRES in native synchronous mode and
 Multisplitting algorithms in asynchronous mode}
@@ -624,12 +623,12 @@ Multisplitting algorithms in asynchronous mode}
 The previous paragraphs put in evidence the interests to simulate the
 behavior of the application before any deployment in a real environment.
 We have focused the study on analyzing the performance in varying the
-key factors impacting the results. In the same line, the study compares
-the performance of the two proposed methods in \textbf{synchronous mode
-}. In this section, with the same previous methodology, the goal is to
-demonstrate the efficiency of the multisplitting method in \textbf{
-asynchronous mode} compare with the classical GMRES staying in the
-synchronous mode.
+key factors impacting the results. The study compares
+the performance of the two proposed algorithms both in \textit{synchronous mode
+}. In this section, following the same previous methodology, the goal is to
+demonstrate the efficiency of the multisplitting method in \textit{
+asynchronous mode} compared with the classical GMRES staying in
+\textit{synchronous mode}.
 
 Note that the interest of using the asynchronous mode for data exchange
 is mainly, in opposite of the synchronous mode, the non-wait aspects of
@@ -639,11 +638,10 @@ calculation without waiting for the end of the communication. Thus, the
 asynchronous may theoretically reduce the overall execution time and can
 improve the algorithm performance.
 
-As stated supra, SIMGRID simulator tool has been used to prove the
+As stated supra, Simgrid simulator tool has been used to prove the
 efficiency of the multisplitting in asynchronous mode and to find the
 best combination of the grid resources (CPU, Network, input matrix size,
-\ldots ) to get the highest "\,relative gain" in comparison with the
-classical GMRES time.
+\ldots ) to get the highest \textit{"relative gain"} (exec\_time$_{GMRES}$ / exec\_time$_{multisplitting}$) in comparison with the classical GMRES time.
 
 
 The test conditions are summarized in the table below : \\
@@ -665,7 +663,7 @@ Again, comprehensive and extensive tests have been conducted varying the
 CPU power and the network parameters (bandwidth and latency) in the
 simulator tool with different problem size. The relative gains greater
 than 1 between the two algorithms have been captured after each step of
-the test. Table I below has recorded the best grid configurations
+the test. Table 7 below has recorded the best grid configurations
 allowing the multisplitting method execution time more performant 2.5 times than
 the classical GMRES execution and convergence time. The experimentation has demonstrated the relative multisplitting algorithm tolerance when using a low speed network that we encounter usually with distant clusters thru the internet.
 
@@ -677,11 +675,13 @@ the classical GMRES execution and convergence time. The experimentation has demo
                   |*{#1}{>{\centering\arraybackslash}p{\mytablew}|}}}{%
     \end{tabular}}
 
+
 \begin{table}[!t]
   \centering
-  \caption{Relative gain of the multisplitting algorithm compared with
-the classical GMRES}
-  \label{"Table 7"}
+%  \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
+%  \label{"Table 7"}
+Table 7. Relative gain of the multisplitting algorithm compared with
+the classical GMRES \\
 
   \begin{mytable}{11}
     \hline
