X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/34ef1c761f30fa1a22267a93d9aeaabb90869ada..538afc25a7a90630d2f90891d0a4d700bfe3460f:/paper.tex?ds=inline diff --git a/paper.tex b/paper.tex index beb3140..a2e23a2 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 @@ -536,60 +528,57 @@ 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. -%\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 - -\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?} +Table~\ref{tab:01} summarizes the parameters used in the different simulations: the grid architectures, the network of inter-clusters backbone links and the matrix sizes of the 3D Poisson problem. However, for all simulations we fix the network parameters of the intra-clusters links: the bandwidth $bw$=10Gbs and the latency $lat$=8$\times$10$^{-6}$. In what follows, we will present the test conditions, the output results and our comments. \begin{table} [ht!] \begin{center} -\begin{tabular}{ll } - \hline - Grid architecture & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\ %\hline - \multirow{2}{*}{Network} & Inter (N2): $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ %\hline - & Intra (N1): $bw$=10Gbs, $lat$=8$\times$10$^{-6}$ \\ - \multirow{2}{*}{Matrix size} & N$_{x}$ $\times$ N$_{y}$ $\times$ N$_{z}$ =150 $\times$ 150 $\times$ 150\\ %\hline - & N$_{x}$ $\times$ N$_{y}$ $\times$ N$_{z}$ =170 $\times$ 170 $\times$ 170 \\ \hline - \end{tabular} -\caption{Test conditions: various grid configurations with the matrix sizes 150$^3$ or 170$^3$} -%\LZK{Ce sont les caractéristiques du réseau intra ou inter clusters? Ce n'est pas précisé...} -%\RCE{oui c est precise} +\begin{tabular}{ll} +\hline +Grid architecture & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\ +\multirow{2}{*}{Network inter-clusters} & $N1$: $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ + & $N2$: $bw$=10Gbs, $lat$=8$\times$10$^{-6}$ \\ +\multirow{2}{*}{Matrix size} & $Mat1$: N$_{x}\times$N$_{y}\times$N$_{z}$=150$\times$150$\times$150\\ + & $Mat2$: N$_{x}\times$N$_{y}\times$N$_{z}$=170$\times$170$\times$170 \\ \hline +\end{tabular} +\caption{Parameters for the different simulations} \label{tab:01} \end{center} \end{table} + +\subsubsection{Simulations for various grid architectures and scaling-up matrix sizes} +\ \\ +% environment +In this section, we analyze the simulations conducted on various grid configurations and for different sizes of the 3D Poisson problem. The parameters of the network between clusters is fixed to $N1$ (see Table~\ref{tab:01}. Figure~\ref{fig:01} shows, for all grid configurations and a given matrix size 170$^3$ elements, a non-variation in the number of iterations for the classical GMRES algorithm, which is not the case of the Krylov two-stage algorithm. + + + + + + + + + + + + + + + + + + + + + + + + -In this section, we analyze the simulations conducted on various grid -configurations presented in Table~\ref{tab:01}. It should be noticed that two -networks are considered: N1 is the network between clusters (inter-cluster) and -N2 is the network inside a cluster (intra-cluster). Figure~\ref{fig:01} shows, -for all grid configurations and a given matrix size, a non-variation in the -number of iterations for the classical GMRES algorithm, which is not the case of -the Krylov two-stage algorithm. -%% First, the results in Figure~\ref{fig:01} -%% show for all grid configurations 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. -%\RC{CE attention tu n'as pas mis de label dans tes figures, donc c'est le bordel, j'en mets mais vérifie...} -%\RC{Les légendes ne sont pas explicites...} -%\RCE{Corrige} \begin{figure} [htbp] \begin{center} @@ -644,9 +633,9 @@ 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 +659,19 @@ 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 +684,9 @@ 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} @@ -723,7 +714,7 @@ of $40\%$ which is only around $24\%$ for the classical GMRES. \hline Grid Architecture & 4 $\times$ 8\\ %\hline Inter Network & $bw$=1Gbs - $lat$=5.10$^{-5}$ \\ - Input matrix size & $N_{x} \times N_{y} \times N_{z}$ = From 40$^{3}$ to 200$^{3}$\\ \hline + Input matrix size & $N_{x} \times N_{y} \times N_{z}$ = From 50$^{3}$ to 190$^{3}$\\ \hline \end{tabular} \caption{Test conditions: Input matrix size impacts} \label{tab:05} @@ -737,20 +728,15 @@ of $40\%$ which is only around $24\%$ for the classical GMRES. \label{fig:05} \end{figure} -In these experiments, 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 Figure~\ref{fig:05}, the execution -time for both algorithms increases when the input matrix size also increases. -But the interesting results are: -\begin{enumerate} - \item the important increase ($10$ times) of the number of iterations needed to - reach the convergence for the classical GMRES algorithm particularly, when the matrix size - go beyond $N_{x}=150$; \RC{C'est toujours pas clair... ok le nommbre d'itérations est 10 fois plus long mais la suite de la phrase ne veut rien dire} - \RCE{Le nombre d'iterations augmente de 10 fois, cela surtout a partir de N=150} - -\item the classical GMRES execution time is almost the double for $N_{x}=140$ - compared with the Krylov multisplitting method. -\end{enumerate} +In these experiments, the input matrix size has been set from $50^3$ to +$190^3$. Obviously, as shown in Figure~\ref{fig:05}, the execution time for both +algorithms increases when the input matrix size also increases. For all problem +sizes, GMRES is always slower than the Krylov multisplitting. Moreover, for this +benchmark, it seems that the greater the problem size is, the bigger the ratio +between both algorithm execution times is. We can also observ that for some +problem sizes, the Krylov multisplitting convergence varies quite a +lot. Consequently the execution times in that cases also varies. + 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