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-\documentclass[conference]{IEEEtran}
+%\documentclass[conference]{IEEEtran}
+\documentclass[times]{cpeauth}
+
+\usepackage{moreverb}
+
+%\usepackage[dvips,colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref}
+
+%\newcommand\BibTeX{{\rmfamily B\kern-.05em \textsc{i\kern-.025em b}\kern-.08em
+%T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
+
+\def\volumeyear{2015}
+
+\usepackage{graphicx}
+\usepackage{wrapfig}
+\usepackage{grffile}
 
 \usepackage[T1]{fontenc}
 \usepackage[utf8]{inputenc}
@@ -23,6 +37,7 @@
 
 \usepackage{xspace}
 \usepackage[textsize=footnotesize]{todonotes}
+
 \newcommand{\AG}[2][inline]{%
   \todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}
 \newcommand{\RC}[2][inline]{%
@@ -48,35 +63,35 @@
 \newcolumntype{g}{>{\columncolor{Gray}}c}
 \definecolor{Gray}{gray}{0.9}
 
+
+
 \begin{document}
 \RCE{Titre a confirmer.}
 \title{Comparative performance analysis of simulated grid-enabled numerical iterative algorithms}
+%\itshape{\journalnamelc}\footnotemark[2]}
 
-\author{%
-  \IEEEauthorblockN{%
-    Charles Emile Ramamonjisoa and
+\author{    Charles Emile Ramamonjisoa and
     David Laiymani and
     Arnaud Giersch and
     Lilia Ziane Khodja and
     RaphaÃ«l Couturier
-  }
-  \IEEEauthorblockA{%
+}
+
+\address{
+	\centering    
     Femto-ST Institute - DISC Department\\
     UniversitÃ© de Franche-ComtÃ©\\
     Belfort\\
     Email: \email{{raphael.couturier,arnaud.giersch,david.laiymani,charles.ramamonjisoa}@univ-fcomte.fr}
-  }
 }
 
-\maketitle
-
 \begin{abstract}
 ABSTRACT
+\end{abstract}
 
+\keywords{Algorithm; distributed; iterative; asynchronous; simulation; simgrid; performance}
 
-Keywords : Algorithm distributed iterative asynchronous simulation simgrid performance
-
-\end{abstract}
+\maketitle
 
 \section{Introduction}
 
@@ -86,15 +101,414 @@ Keywords : Algorithm distributed iterative asynchronous simulation simgrid perfo
 
 \section{Simulation of the multisplitting method}
 
-\section{Experiments and results}
-
-\paragraph*{1. Study setup and methodology}
-
-\paragraph*{2. Factors impacting distributed applications performance in a grid environment}
-
-\paragraph*{3. Comparing GMRES and Multisplitting algorithms in synchronous mode}
-
-\paragraph*{4. Comparing GMRES in synchronous mode and Multisplitting algorithms in asynchronous mode}
+\section{Experimental, Results and Comments}
+
+
+\textbf{V.1. Setup study and Methodology}
+
+To conduct our study, we have put in place the following methodology 
+which can be reused with any grid-enabled applications.
+
+\textbf{Step 1} : Choose with the end users the class of algorithms or 
+the application to be tested. Numerical parallel iterative algorithms 
+have been chosen for the study in the paper. 
+
+\textbf{Step 2} : Collect the software materials needed for the 
+experimentation. In our case, we have three variants algorithms for the 
+resolution of three 3D-Poisson problem: (1) using the classical GMRES 
+\textit{(Generalized Minimal RESidual Method)} alias Algo-1 in this 
+paper, (2) using the multisplitting method alias Algo-2 and (3) an 
+enhanced version of the multisplitting method as Algo-3. 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 $[$10$]$ 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 
+in one hand the algorithm execution mode (synchronous and asynchronous) 
+and in the other hand the execution time and the number of iterations of 
+the application before obtaining the convergence.
+
+\textbf{Step 4 }: Setup up the different grid testbeds environment 
+which will be simulated in the simulator tool to run the program. The 
+following architecture has been configured in Simgrid : 2x16 - that is a 
+grid containing 2 clusters with 16 hosts (processors/cores) each -, 4x8, 
+4x16, 8x8 and 2x50. The network has been designed to operate with a 
+bandwidth equals to 10Gbits (resp. 1Gbits/s) and a latency of 8E-6 
+microseconds (resp. 5E-5) for the intra-clusters links (resp. 
+inter-clusters backbone links).
+
+\textbf{Step 5}: Process an extensive and comprehensive testings 
+within these configurations in varying the key parameters, especially 
+the CPU power capacity, the network parameters and also the size of the 
+input matrix. Note that some parameters should be invariant to allow the 
+comparison like some program input arguments.
+
+\textbf{Step 6} : Collect and analyze the output results.
+
+\textbf{ V.2. Factors impacting distributed applications performance in 
+a grid environment}
+
+From our previous experience on running distributed application in a 
+computational grid, many factors are identified to have an impact on the 
+program behavior and performance on this specific environment. Mainly, 
+first of all, the architecture of the grid itself can obviously 
+influence the performance results of the program. The performance gain 
+might be important theoretically when the number of clusters and/or the 
+number of nodes (processors/cores) in each individual cluster increase. 
+
+Another important factor impacting the overall performance of the 
+application is the network configuration. Two main network parameters 
+can modify drastically the program output results : (i) the network 
+bandwidth (bw=bits/s) also known as "the data-carrying capacity" 
+$[$13$]$ of the network is defined as the maximum of data that can pass 
+from one point to another in a unit of time. (ii) the network latency 
+(lat : microsecond) defined as the delay from the start time to send the 
+data from a source and the final time the destination have finished to 
+receive it. Upon the network characteristics, another impacting factor 
+is the application dependent volume of data exchanged between the nodes 
+in the cluster and between distant clusters. Large volume of data can be 
+transferred in transit between the clusters and nodes during the code 
+execution. 
+
+ In a grid environment, it is common to distinguish in one hand, the 
+"\,intra-network" which refers to the links between nodes within a 
+cluster and in the other hand, the "\,inter-network" which is the 
+backbone link between clusters. By design, these two networks perform 
+with different speed. The intra-network generally works like a high 
+speed local network with a high bandwith and very low latency. In 
+opposite, the inter-network connects clusters sometime via heterogeneous 
+networks components thru internet with a lower speed. The network 
+between distant clusters might be a bottleneck for the global 
+performance of the application. 
+
+\textbf{V.3 Comparing GMRES and Multisplitting algorithms in 
+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 
+should mean 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 
+simulator will confirm the targeted outcomes, particularly for poor and 
+slow networks, focusing on the impact on the communication performance 
+on the chosen class of algorithm $[$12$]$.
+
+The following paragraphs present the test conditions, the output results 
+and our comments.
+
+
+\textit{3.a Executing the algorithms on various computational grid 
+architecture scaling up the input matrix size}
+\\
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x16, 4x8, 4x16 and 8x8\\ %\hline
+ Network & N2 : bw=1Gbs-lat=5E-05 \\ %\hline
+ Input matrix size & N$_{x}$ =150 x 150 x 150 and\\ %\hline
+ - & N$_{x}$ =170 x 170 x 170    \\ \hline
+ \end{tabular}
+\end{footnotesize}
+
+
+ Table 1 : Clusters x Nodes with NX=150 or NX=170
+
+\RCE{J'ai voulu mettre les tableaux des donnÃ©es mais je pense que c'est inutile et Ã§a va surcharger}
+
+
+The results in figure 1 show the non-variation of the number of 
+iterations of classical GMRES for a given input matrix size; it is not 
+the case for the multisplitting method. 
+
+%%\begin{wrapfigure}{l}{60mm}
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{Cluster x Nodes NX=150 and NX=170.jpg}
+\caption{Cluster x Nodes NX=150 and NX=170 \label{overflow}}
+\end{figure}
+%%\end{wrapfigure}
+
+
+Unless the 8x8 cluster, the time 
+execution difference between the two algorithms is important when 
+comparing between different grid architectures, even with the same number of 
+processors (like 2x16 and 4x8 = 32 processors for example). The 
+experiment concludes the low sensitivity of the multisplitting method 
+(compared with the classical GMRES) when scaling up to higher input 
+matrix size. 
+
+\textit{3.b Running on various computational grid architecture}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x16, 4x8\\ %\hline
+ Network & N1 : bw=10Gbs-lat=8E-06 \\ %\hline
+ - & N2 : bw=1Gbs-lat=5E-05 \\
+ Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline \\
+ \end{tabular}
+\end{footnotesize}
+
+%Table 2 : Clusters x Nodes - Networks N1 x N2
+%\RCE{idem pour tous les tableaux de donnees}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{Cluster x Nodes N1 x N2.jpg}
+\caption{Cluster x Nodes N1 x N2\label{overflow}}
+\end{figure}
+
+The experiments compare the behavior of the algorithms running first on 
+speed inter- cluster network (N1) and a less performant network (N2). 
+The figure 2 shows that end users will gain to reduce the execution time 
+for both algorithms in using a grid architecture like 4x16 or 8x8: the 
+performance was increased in a factor of 2. The results depict also that 
+when the network speed drops down, the difference between the execution 
+times can reach more than 25\%. 
+
+\textit{\\\\\\\\\\\\\\\\\\3.c Network latency impacts on performance}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x16\\ %\hline
+ Network & N1 : bw=1Gbs \\ %\hline
+ Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline\\
+ \end{tabular}
+\end{footnotesize}
+
+Table 3 : Network latency impact
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{Network latency impact on execution time.jpg}
+\caption{Network latency impact on execution time\label{overflow}}
+\end{figure}
+
+
+According the results in table and figure 3, degradation of the network 
+latency from 8.10$^{-6}$ to 6.10$^{-5}$ implies an absolute time 
+increase more than 75\% (resp. 82\%) of the execution for the classical 
+GMRES (resp. multisplitting) algorithm. In addition, it appears that the 
+multisplitting method tolerates more the network latency variation with 
+a less rate increase. Consequently, in the worst case (lat=6.10$^{-5
+}$), the execution time for GMRES is almost the double of the time for 
+the multisplitting, even though, the performance was on the same order 
+of magnitude with a latency of 8.10$^{-6}$. 
+
+\textit{3.d Network bandwidth impacts on performance}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x16\\ %\hline
+ Network & N1 : bw=1Gbs - lat=5E-05 \\ %\hline
+ Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline
+ \end{tabular}
+\end{footnotesize}
+
+Table 4 : Network bandwidth impact
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{Network bandwith impact on execution time.jpg}
+\caption{Network bandwith impact on execution time\label{overflow}}
+\end{figure}
+
+
+
+The results of increasing the network bandwidth depict the improvement 
+of the performance by reducing the execution time for both of the two 
+algorithms. However, and again in this case, the multisplitting method 
+presents a better performance in the considered bandwidth interval with 
+a gain of 40\% which is only around 24\% for classical GMRES.
+
+\textit{3.e Input matrix size impacts on performance}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 4x8\\ %\hline
+ Network & N2 : bw=1Gbs - lat=5E-05 \\ %\hline
+ Input matrix size & N$_{x}$ = From 40 to 200\\ \hline
+ \end{tabular}
+\end{footnotesize}
+
+Table 5 : Input matrix size impact
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{Pb size impact on execution time.jpg}
+\caption{Pb size impact on execution time\label{overflow}}
+\end{figure}
+
+In this experimentation, the input matrix size has been set from 
+Nx=Ny=Nz=40 to 200 side elements that is from 40$^{3}$ = 64.000 to 
+200$^{3}$ = 8.000.000 points. Obviously, as shown in the figure 5, 
+the execution time for the algorithms convergence increases with the 
+input matrix size. But the interesting result here direct on (i) the 
+drastic increase (300 times) of the number of iterations needed before 
+the convergence for the classical GMRES algorithm when the matrix size 
+go beyond Nx=150; (ii) the classical GMRES execution time also almost 
+the double from Nx=140 compared with the convergence time of the 
+multisplitting method. These findings may help a lot end users to setup 
+the best and the optimal targeted environment for the application 
+deployment when focusing on the problem size scale up. Note that the 
+same test has been done with the grid 2x16 getting the same conclusion.
+
+\textit{3.f CPU Power impact on performance}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x16\\ %\hline
+ Network & N2 : bw=1Gbs - lat=5E-05 \\ %\hline
+ Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline
+ \end{tabular}
+\end{footnotesize}
+
+Table 6 : CPU Power impact
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=60mm]{CPU Power impact on execution time.jpg}
+\caption{CPU Power impact on execution time\label{overflow}}
+\end{figure}
+
+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.
+
+ \textbf{V.4 Comparing GMRES in native synchronous mode and 
+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.
+
+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 
+the current computation after a communication operation like sending 
+some data between nodes. Each processor can continue their local 
+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 
+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. 
+
+
+The test conditions are summarized in the table below : 
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline  
+ Grid & 2x50 totaling 100 processors\\ %\hline
+ Processors & 1 GFlops to 1.5 GFlops\\
+   Intra-Network & bw=1.25 Gbits - lat=5E-05 \\ %\hline
+   Inter-Network & bw=5 Mbits - lat=2E-02\\
+ Input matrix size & N$_{x}$ = From 62 to 150\\ %\hline
+ Residual error precision: 10$^{-5}$ to 10$^{-9}$\\ \hline
+ \end{tabular}
+\end{footnotesize}
+
+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 
+allowing a multiplitting method time more than 2.5 times lower than 
+classical GMRES execution and convergence time. The finding thru this 
+experimentation is the tolerance of the multisplitting method under a 
+low speed network that we encounter usually with distant clusters thru the 
+internet.
+
+% use the same column width for the following three tables
+\newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}
+\newenvironment{mytable}[1]{% #1: number of columns for data
+  \renewcommand{\arraystretch}{1.3}%
+  \begin{tabular}{|>{\bfseries}r%
+                  |*{#1}{>{\centering\arraybackslash}p{\mytablew}|}}}{%
+    \end{tabular}}
+
+\begin{table}[!t]
+  \centering
+  \caption{Relative gain of the multisplitting algorithm compared with 
+the classical GMRES}
+  \label{tab.cluster.2x50}
+
+  \begin{mytable}{6}
+    \hline
+    bw
+    & 5         & 5         & 5         & 5         & 5         & 50 \\
+    \hline
+    lat
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
+    \hline
+    power
+    & 1         & 1         & 1         & 1.5       & 1.5       & 1.5 \\
+    \hline
+    size
+    & 62        & 62        & 62        & 100       & 100       & 110 \\
+    \hline
+    Prec/Eprec
+    & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    \hline
+    speedup
+    & 0.396     & 0.392     & 0.396     & 0.391     & 0.393     & 0.395 \\
+    \hline
+  \end{mytable}
+
+  \smallskip
+
+  \begin{mytable}{6}
+    \hline
+    bw
+    & 50        & 50        & 50        & 50        & 10        & 10 \\
+    \hline
+    lat
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.03      & 0.01 \\
+    \hline
+    power
+    & 1.5       & 1.5       & 1.5       & 1.5       & 1         & 1.5 \\
+    \hline
+    size
+    & 120       & 130       & 140       & 150       & 171       & 171 \\
+    \hline
+    Prec/Eprec
+    & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-5}  & \np{E-5} \\
+    \hline
+    speedup
+    & 0.398     & 0.388     & 0.393     & 0.394     & 0.63      & 0.778 \\
+    \hline
+  \end{mytable}
+\end{table}
 
 \section{Conclusion}
 CONCLUSION
