X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/34ef1c761f30fa1a22267a93d9aeaabb90869ada..3b175ad474db1b6208b1115e91d0b5bfde6f0288:/paper.tex?ds=sidebyside diff --git a/paper.tex b/paper.tex index beb3140..60fcffc 100644 --- a/paper.tex +++ b/paper.tex @@ -407,10 +407,10 @@ in which several clusters are geographically distant, so there are intra and inter-cluster communications. In the following, these parameters are described: \begin{itemize} - \item hostfile: hosts description file. + \item hostfile: hosts description file, \item platform: file describing the platform architecture: clusters (CPU power, \dots{}), intra cluster network description, inter cluster network (bandwidth $bw$, -latency $lat$, \dots{}). +latency $lat$, \dots{}), \item archi : grid computational description (number of clusters, number of nodes/processors in each cluster). \end{itemize} @@ -442,8 +442,6 @@ In this section, experiments for both multisplitting algorithms are reported. Fi \subsection{The 3D Poisson problem} \label{3dpoisson} - - We use our two-stage algorithms to solve the well-known Poisson problem $\nabla^2\phi=f$~\cite{Polyanin01}. In three-dimensional Cartesian coordinates in $\mathbb{R}^3$, the problem takes the following form: \begin{equation} \frac{\partial^2}{\partial x^2}\phi(x,y,z)+\frac{\partial^2}{\partial y^2}\phi(x,y,z)+\frac{\partial^2}{\partial z^2}\phi(x,y,z)=f(x,y,z)\mbox{~in the domain~}\Omega @@ -485,17 +483,11 @@ results comparison and analysis. In the scope of this study, we retain on the one hand the algorithm execution mode (synchronous and asynchronous) and on the other hand the execution time and the number of iterations to reach the convergence. \\ -\textbf{Step 4 }: Set up the different grid testbed environments that will be +\textbf{Step 4}: Set up the different grid testbed environments that will be simulated in the simulator tool to run the program. The following architectures have been configured in SimGrid : 2$\times$16, 4$\times$8, 4$\times$16, 8$\times$8 and 2$\times$50. The first number represents the number of clusters in the grid and the second number represents -the number of hosts (processors/cores) in each cluster. The network has been -designed to operate with a bandwidth equals to 10Gbits (resp. 1Gbits/s) and a -latency of 8.10$^{-6}$ seconds (resp. 5.10$^{-5}$) for the intra-clusters links -(resp. inter-clusters backbone links). \\ - -\LZK{Il me semble que le bw et lat des deux réseaux varient dans les expés d'une simu à l'autre. On vire la dernière phrase?} -\RC{il me semble qu'on peut laisser ca} +the number of hosts (processors/cores) in each cluster. \\ \textbf{Step 5}: Conduct an extensive and comprehensive testings within these configurations by varying the key parameters, especially @@ -536,26 +528,17 @@ and between distant clusters. This parameter is application dependent. a lower speed. The network between distant clusters might be a bottleneck for the global performance of the application. -\subsection{Comparison of GMRES and Krylov two-stage algorithms in synchronous mode} - -In the scope of this paper, our first objective is to analyze when the Krylov -two-stage method has better performance than the classical GMRES method. With a synchronous iterative method, better performance means a -smaller number of iterations and execution time before reaching the convergence. -In what follows, we will present the test conditions, the output results and our comments. - -%%RAPH : on vire ca, c'est pas clair et pas important -%For a systematic study, the experiments should figure out that, for various -%grid parameters values, the simulator will confirm Multisplitting method better performance compared to classical GMRES, particularly on poor and slow networks. -%\LZK{Pas du tout claire la dernière phrase (For a systematic...)!!} -%\RCE { Reformule autrement} +\subsection{Comparison between GMRES and two-stage multisplitting algorithms in synchronous mode} +In the scope of this paper, our first objective is to analyze when the synchronous Krylov two-stage method has better performance than the classical GMRES method. With a synchronous iterative method, better performance means a smaller number of iterations and execution time before reaching the convergence. In what follows, we will present the test conditions, the output results and our comments. For all simulations, we fix the network parameters of the intra-cluster links: the bandwidth $bw$=10Gbs and the latency $lat$=8$\times$10$^{-6}$. - -%\subsubsection{Execution of the algorithms on various computational grid architectures and scaling up the input matrix size} \subsubsection{Simulations for various grid architectures and scaling-up matrix sizes} -\ \\ +\ \\ % environment + The network of intra-clusters links has been +designed to operate with a bandwidth equals to 10Gbits and a latency of 8$\times$10$^{-6}$ seconds. \\ + \RC{Je ne comprends plus rien CE : pourquoi dans 5.4.1 il y a 2 network et aussi dans 5.4.2. Quelle est la différence? Dans la figure 3 de la section 5.4.1 pourquoi il n'y a pas N1 et N2?} \begin{table} [ht!] @@ -644,9 +627,9 @@ the network speed drops down (variation of 12.5\%), the difference between t \begin{figure} [htbp] \centering \includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf} -\caption{Various grid configurations with networks N1 vs N2 -\AG{\np{8E-6}, \np{5E-6} au lieu de 8E-6, 5E-6}} -\RCE{Corrige} +\caption{Various grid configurations with networks N1 vs N2} +%\AG{\np{8E-6}, \np{5E-6} au lieu de 8E-6, 5E-6}} +%\RCE{Corrige} \label{fig:02} \end{figure} %\end{wrapfigure} @@ -670,18 +653,19 @@ the network speed drops down (variation of 12.5\%), the difference between t \begin{figure} [htbp] \centering \includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf} -\caption{Network latency impacts on execution time -\AG{\np{E-6}}} +\caption{Network latency impacts on execution time} +%\AG{\np{E-6}}} \label{fig:03} \end{figure} -According to the results of Figure~\ref{fig:03}, a degradation of the network -latency from $8.10^{-6}$ to $6.10^{-5}$ implies an absolute time increase of -more than $75\%$ (resp. $82\%$) of the execution for the classical GMRES -(resp. Krylov multisplitting) algorithm which means that the GMRES seems tolerate more the network latency variation with a less rate increase of the execution time. However, the execution time factor between the two algorithms varies from 2.2 to 1.5 times with a network latency decreasing from $8.10^{-6}$ to $6.10^{-5}$. +In Table~\ref{tab:03}, parameters for the influence of the network latency are +reported. According to the results of Figure~\ref{fig:03}, a degradation of the +network latency from $8.10^{-6}$ to $6.10^{-5}$ implies an absolute time +increase of more than $75\%$ (resp. $82\%$) of the execution for the classical +GMRES (resp. Krylov multisplitting) algorithm. The execution time factor +between the two algorithms varies from 2.2 to 1.5 times with a network latency +decreasing from $8.10^{-6}$ to $6.10^{-5}$. -\RC{Les 2 précédentes phrases me semblent en contradiction....} -\RCE{Reformule} \subsubsection{Network bandwidth impacts on performance} \ \\ @@ -694,8 +678,9 @@ more than $75\%$ (resp. $82\%$) of the execution for the classical GMRES & $lat$= 5.10$^{-5}$ second \\ Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline \\ \end{tabular} -\caption{Test conditions: Network bandwidth impacts\RC{Qu'est ce qui varie ici? Il n'y a pas de variation dans le tableau}} -\RCE{C est le bw} +\caption{Test conditions: Network bandwidth impacts} +% \RC{Qu'est ce qui varie ici? Il n'y a pas de variation dans le tableau} +%\RCE{C est le bw} \label{tab:04} \end{table}