+\section{SimGrid}
+ \label{sec:simgrid}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\section{Two-stage multisplitting methods}
+\label{sec:04}
+\subsection{Synchronous and asynchronous two-stage methods for sparse linear systems}
+\label{sec:04.01}
+In this paper we focus on two-stage multisplitting methods in their both versions synchronous and asynchronous~\cite{Frommer92,Szyld92,Bru95}. These iterative methods are based on multisplitting methods~\cite{O'leary85,White86,Alefeld97} and use two nested iterations: the outer iteration and the inner iteration. Let us consider the following sparse linear system of $n$ equations in $\mathbb{R}$
+\begin{equation}
+Ax=b,
+\label{eq:01}
+\end{equation}
+where $A$ is a sparse square and nonsingular matrix, $b$ is the right-hand side and $x$ is the solution of the system. Our work in this paper is restricted to the block Jacobi splitting method. This approach of multisplitting consists in partitioning the matrix $A$ into $L$ horizontal band matrices of order $\frac{n}{L}\times n$ without overlapping (i.e. sub-vectors $\{x_\ell\}_{1\leq\ell\leq L}$ are disjoint). The two-stage multisplitting methods solve the linear system~(\ref{eq:01}) iteratively as follows
+\begin{equation}
+x_\ell^{k+1} = A_{\ell\ell}^{-1}(b_\ell - \displaystyle\sum^{L}_{\substack{m=1\\m\neq\ell}}{A_{\ell m}x^k_m}),\mbox{~for~}\ell=1,\ldots,L\mbox{~and~}k=1,2,3,\ldots
+\label{eq:02}
+\end{equation}
+where $x_\ell$ are sub-vectors of the solution $x$, $b_\ell$ are the sub-vectors of the right-hand side $b$, and $A_{\ell\ell}$ and $A_{\ell m}$ are diagonal and off-diagonal blocks of matrix $A$ respectively. The iterations of these methods can naturally be computed in parallel such that each processor or cluster of processors is responsible for solving one splitting as a linear sub-system
+\begin{equation}
+A_{\ell\ell} x_\ell = c_\ell,\mbox{~for~}\ell=1,\ldots,L,
+\label{eq:03}
+\end{equation}
+where right-hand sides $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m$ are computed using the shared vectors $x_m$. In this paper, we use the well-known iterative method GMRES ({\it Generalized Minimal RESidual})~\cite{saad86} as an inner iteration to approximate the solutions of the different splittings arising from the block Jacobi multisplitting of matrix $A$. The algorithm in Figure~\ref{alg:01} shows the main key points of our block Jacobi two-stage method executed by a cluster of processors. In line~\ref{solve}, the linear sub-system~(\ref{eq:03}) is solved in parallel using GMRES method where $\MIG$ and $\TOLG$ are the maximum number of inner iterations and the tolerance threshold for GMRES respectively. The convergence of the two-stage multisplitting methods, based on synchronous or asynchronous iterations, is studied by many authors for example~\cite{Bru95,bahi07}.
+
+\begin{figure}[t]
+%\begin{algorithm}[t]
+%\caption{Block Jacobi two-stage multisplitting method}
+\begin{algorithmic}[1]
+ \Input $A_\ell$ (sparse matrix), $b_\ell$ (right-hand side)
+ \Output $x_\ell$ (solution vector)\vspace{0.2cm}
+ \State Set the initial guess $x^0$
+ \For {$k=1,2,3,\ldots$ until convergence}
+ \State $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m^{k-1}$
+ \State $x^k_\ell=Solve_{gmres}(A_{\ell\ell},c_\ell,x^{k-1}_\ell,\MIG,\TOLG)$\label{solve}
+ \State Send $x_\ell^k$ to neighboring clusters\label{send}
+ \State Receive $\{x_m^k\}_{m\neq\ell}$ from neighboring clusters\label{recv}
+ \EndFor
+\end{algorithmic}
+\caption{Block Jacobi two-stage multisplitting method}
+\label{alg:01}
+%\end{algorithm}
+\end{figure}
+
+In this paper, we propose two algorithms of two-stage multisplitting methods. The first algorithm is based on asynchronous model which allows the communications to be overlapped by computations and reduces the idle times resulting from the synchronizations. So in the asynchronous mode, our two-stage algorithm uses asynchronous outer iterations and asynchronous communications between clusters. The communications (i.e. lines~\ref{send} and~\ref{recv} in Figure~\ref{alg:01}) are performed by message passing using MPI non-blocking communication routines. The convergence of the asynchronous iterations is detected when all clusters have locally converged
+\begin{equation}
+k\geq\MIM\mbox{~or~}\|x_\ell^{k+1}-x_\ell^k\|_{\infty }\leq\TOLM,
+\label{eq:04}
+\end{equation}
+where $\MIM$ is the maximum number of outer iterations and $\TOLM$ is the tolerance threshold for the two-stage algorithm.
+
+The second two-stage algorithm is based on synchronous outer iterations. We propose to use the Krylov iteration based on residual minimization to improve the slow convergence of the multisplitting methods. In this case, a $n\times s$ matrix $S$ is set using solutions issued from the inner iteration
+\begin{equation}
+S=[x^1,x^2,\ldots,x^s],~s\ll n.
+\label{eq:05}
+\end{equation}
+At each $s$ outer iterations, the algorithm computes a new approximation $\tilde{x}=S\alpha$ which minimizes the residual
+\begin{equation}
+\min_{\alpha\in\mathbb{R}^s}{\|b-AS\alpha\|_2}.
+\label{eq:06}
+\end{equation}
+The algorithm in Figure~\ref{alg:02} includes the procedure of the residual minimization and the outer iteration is restarted with a new approximation $\tilde{x}$ at every $s$ iterations. The least-squares problem~(\ref{eq:06}) is solved in parallel by all clusters using CGLS method~\cite{Hestenes52} such that $\MIC$ is the maximum number of iterations and $\TOLC$ is the tolerance threshold for this method (line~\ref{cgls} in Figure~\ref{alg:02}).
+
+\begin{figure}[t]
+%\begin{algorithm}[t]
+%\caption{Krylov two-stage method using block Jacobi multisplitting}
+\begin{algorithmic}[1]
+ \Input $A_\ell$ (sparse matrix), $b_\ell$ (right-hand side)
+ \Output $x_\ell$ (solution vector)\vspace{0.2cm}
+ \State Set the initial guess $x^0$
+ \For {$k=1,2,3,\ldots$ until convergence}
+ \State $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m^{k-1}$
+ \State $x^k_\ell=Solve_{gmres}(A_{\ell\ell},c_\ell,x^{k-1}_\ell,\MIG,\TOLG)$
+ \State $S_{\ell,k\mod s}=x_\ell^k$
+ \If{$k\mod s = 0$}
+ \State $\alpha = Solve_{cgls}(AS,b,\MIC,\TOLC)$\label{cgls}
+ \State $\tilde{x_\ell}=S_\ell\alpha$
+ \State Send $\tilde{x_\ell}$ to neighboring clusters
+ \Else
+ \State Send $x_\ell^k$ to neighboring clusters
+ \EndIf
+ \State Receive $\{x_m^k\}_{m\neq\ell}$ from neighboring clusters
+ \EndFor
+\end{algorithmic}
+\caption{Krylov two-stage method using block Jacobi multisplitting}
+\label{alg:02}
+%\end{algorithm}
+\end{figure}
+
+\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.
+
+
+\paragraph{Simgrid Simulator parameters}
+
+\begin{itemize}
+ \item hostfile: Hosts description file.
+ \item plarform: File describing the platform architecture : clusters (CPU power,
+\dots{}), intra cluster network description, inter cluster network (bandwidth bw,
+latency lat, \dots{}).
+ \item archi : Grid computational description (Number of clusters, Number of
+nodes/processors for each cluster).
+\end{itemize}
+
+
+In addition, the following arguments are given to the programs at runtime:
+
+\begin{itemize}
+ \item Maximum number of inner and outer iterations;
+ \item Inner and outer precisions;
+ \item Matrix size (N$_{x}$, N$_{y}$ and N$_{z}$);
+ \item Matrix diagonal value = 6.0;
+ \item Execution Mode: synchronous or asynchronous.
+\end{itemize}
+
+At last, note that the two solver algorithms have been executed with the Simgrid selector -cfg=smpi/running\_power which determines the computational power (here 19GFlops) of the simulator host machine.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\section{Experimental Results}
+\label{sec:expe}
+
+
+\subsection{Study setup and Simulation Methodology}
+
+To conduct our study, we have put in place the following methodology
+which can be reused for 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 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 the 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
+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 }: Set up the different grid testbed environments
+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 8.10$^{-6}$
+microseconds (resp. 5.10$^{-5}$) for the intra-clusters links (resp.
+inter-clusters backbone links). \\
+
+\textbf{Step 5}: Conduct 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 like some program input arguments should be fixed to be invariant to allow the comparison. \\
+
+\textbf{Step 6} : Collect and analyze the output results.
+
+\subsection{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"
+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 and 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.
+
+\subsection{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
+\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
+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.
+
+The following paragraphs present the test conditions, the output results
+and our comments.\\
+
+
+\textit{3.a Executing the algorithms on various computational grid
+architecture and scaling up the input matrix size}
+\\
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline
+ Grid & 2x16, 4x8, 4x16 and 8x8\\ %\hline
+ Network & N2 : bw=1Gbits/s - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ %\hline
+ - & N$_{x}$ x N$_{y}$ x N$_{z}$ =170 x 170 x 170 \\ \hline
+ \end{tabular}
+Table 1 : Clusters x Nodes with N$_{x}$=150 or N$_{x}$=170 \\
+
+\end{footnotesize}
+
+
+
+%\RCE{J'ai voulu mettre les tableaux des données mais je pense que c'est inutile et ça va surcharger}
+
+
+In this section, we compare the algorithms performance running on various grid configuration (2x16, 4x8, 4x16 and 8x8). First, the results in figure 3 show for all grid configuration 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}{100mm}
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf}
+\caption{Cluster x Nodes N$_{x}$=150 and N$_{x}$=170}
+%\label{overflow}}
+\end{figure}
+%\end{wrapfigure}
+
+The execution time 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 the number of the processors in the grid: in average, the GMRES (resp. Multisplitting) algorithm performs 40\% better (resp. 48\%) less when running from 2x16=32 to 8x8=64 processors.
+
+\textit{\\3.b Running on two different speed cluster inter-networks\\}
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline
+ Grid & 2x16, 4x8\\ %\hline
+ Network & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\hline
+ - & N2 : bw=1Gbs-lat=5.10$^{-5}$ \\
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline \\
+ \end{tabular}
+Table 2 : Clusters x Nodes - Networks N1 x N2 \\
+
+ \end{footnotesize}
+
+
+
+%\begin{wrapfigure}{l}{100mm}
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf}
+\caption{Cluster x Nodes N1 x N2}
+%\label{overflow}}
+\end{figure}
+%\end{wrapfigure}
+
+The experiments compare the behavior of the algorithms running first on
+a speed inter- cluster network (N1) and also on a less performant network (N2).
+Figure 4 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 (12.5\%), 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$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline\\
+ \end{tabular}
+Table 3 : Network latency impact \\
+
+\end{footnotesize}
+
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf}
+\caption{Network latency impact on execution time}
+%\label{overflow}}
+\end{figure}
+
+
+According the results in figure 5, 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 of the execution time. 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=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline \\
+ \end{tabular}
+Table 4 : Network bandwidth impact \\
+
+\end{footnotesize}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf}
+\caption{Network bandwith impact on execution time}
+%\label{overflow}
+\end{figure}
+
+
+
+The results of increasing the network bandwidth show the improvement
+of the performance for both of the two algorithms by reducing the execution time (Figure 6). 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=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ = From 40 to 200\\ \hline \\
+ \end{tabular}
+Table 5 : Input matrix size impact\\
+
+\end{footnotesize}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf}
+\caption{Pb size impact on execution time}
+%\label{overflow}}
+\end{figure}
+
+In this experimentation, the input matrix size has been set from
+N$_{x}$ = N$_{y}$ = N$_{z}$ = 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 7,
+the execution time for the two algorithms convergence increases with the
+input matrix size. But the interesting results 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 N$_{x}$=150; (ii) the classical GMRES execution time also almost
+the double from N$_{x}$=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=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline
+ \end{tabular}
+Table 6 : CPU Power impact \\
+
+\end{footnotesize}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf}
+\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.
+
+\subsection{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. 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
+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 \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 : \\
+
+% environment
+\begin{footnotesize}
+\begin{tabular}{r c }
+ \hline
+ Grid & 2x50 totaling 100 processors\\ %\hline
+ Processors Power & 1 GFlops to 1.5 GFlops\\
+ Intra-Network & bw=1.25 Gbits - lat=5.10$^{-5}$ \\ %\hline
+ Inter-Network & bw=5 Mbits - lat=2.10$^{-2}$\\
+ 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 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.
+
+% 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{"Table 7"}
+Table 7. Relative gain of the multisplitting algorithm compared with
+the classical GMRES \\
+
+ \begin{mytable}{11}
+ \hline
+ bandwidth (Mbit/s)
+ & 5 & 5 & 5 & 5 & 5 & 50 & 50 & 50 & 50 & 50 \\
+ \hline
+ latency (ms)
+ & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\
+ \hline
+ power (GFlops)
+ & 1 & 1 & 1 & 1.5 & 1.5 & 1.5 & 1.5 & 1 & 1.5 & 1.5 \\
+ \hline
+ size (N)
+ & 62 & 62 & 62 & 100 & 100 & 110 & 120 & 130 & 140 & 150 \\
+ \hline
+ Precision
+ & \np{E-5} & \np{E-8} & \np{E-9} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11}\\
+ \hline
+ Relative gain
+ & 2.52 & 2.55 & 2.52 & 2.57 & 2.54 & 2.53 & 2.51 & 2.58 & 2.55 & 2.54 \\
+ \hline
+ \end{mytable}
+\end{table}
