X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/2f78f080350308e2f46d8eff8d66a8e127fee583..ca1429f05161a13a6c9cc1eb4a62dcb8217c06d2:/paper.tex?ds=sidebyside diff --git a/paper.tex b/paper.tex index f9434f9..ab8f9ab 100644 --- a/paper.tex +++ b/paper.tex @@ -24,6 +24,8 @@ % Extension pour les liens intra-documents (tagged PDF) % et l'affichage correct des URL (commande \url{http://example.com}) %\usepackage{hyperref} +\usepackage{multirow} + \usepackage{url} \DeclareUrlCommand\email{\urlstyle{same}} @@ -319,7 +321,7 @@ A_{\ell\ell} x_\ell = c_\ell,\mbox{~for~}\ell=1,\ldots,L, \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~\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, has been studied by many authors for example~\cite{Bru95,bahi07}. -\begin{figure}[t] +\begin{figure}[htpb] %\begin{algorithm}[t] %\caption{Block Jacobi two-stage multisplitting method} \begin{algorithmic}[1] @@ -357,7 +359,7 @@ At each $s$ outer iterations, the algorithm computes a new approximation $\tilde \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{figure}[htbp] %\begin{algorithm}[t] %\caption{Krylov two-stage method using block Jacobi multisplitting} \begin{algorithmic}[1] @@ -405,10 +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} @@ -440,8 +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 @@ -483,14 +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). \\ +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 @@ -531,162 +528,143 @@ 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 Multisplitting algorithms in synchronous mode} -In the scope of this paper, our first objective is to analyze when the Krylov -Multisplitting 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. -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. +\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. -The following paragraphs present the test conditions, the output results -and our comments.\\ - - -\subsubsection{Execution of the algorithms on various computational grid -architectures and scaling up the input matrix size} -\ \\ -% environment +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}{r c } - \hline - Grid Architecture & 2 $\times$ 16, 4 $\times$ 8, 4 $\times$ 16 and 8 $\times$ 8\\ %\hline - Inter Network N2 & bw=1Gbits/s - lat=5.10$^{-5}$ \\ %\hline - Input 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 input matrix size N$_{x}$=N$_{y}$=N$_{z}$=150 or 170 \RC{N2 n'est pas défini..}\RC{Nx est défini, Ny? Nz?} -\AG{La lettre 'x' n'est pas le symbole de la multiplication. Utiliser \texttt{\textbackslash times}. Idem dans le texte, les figures, etc.}} +\begin{tabular}{ll} +\hline +Grid architecture & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\ +\multirow{2}{*}{Network inter-clusters} & $N1$: $bw$=10Gbs, $lat$=8$\times$10$^{-6}$ \\ + & $N2$: $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ +\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\\} +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 $N2$ (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 fact, with multisplitting algorithms, the number of splitting (in +our case, it is the number of clusters) influences on the convergence speed. The +higher the number of splitting is, the slower the convergence of the algorithm +is (see the output results obtained from configurations 2$\times$16 vs. 4$\times$8 and configurations 4$\times$16 vs. 8$\times$8). +The execution times between both algorithms is significant with different grid architectures. The synchronous Krylov two-stage algorithm presents better performances than the GMRES algorithm, even for a high number of clusters (about $32\%$ more efficient on a grid of 8$\times$8 than GMRES). In addition, we can observe a better sensitivity of the Krylov two-stage algorithm (compared to the GMRES one) when scaling up the number of the processors in the computational grid: the Krylov two-stage algorithm is about $48\%$ and the GMRES algorithm is about $40\%$ better on 64 processors (grid of 8$\times$8) than 32 processors (grid of 2$\times$16). +\begin{figure}[t] +\begin{center} +\includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf} +\end{center} +\caption{Various grid configurations with the matrix sizes 150$^3$ and 170$^3$} +\label{fig:01} +\end{figure} -In this section, we analyze the performance of algorithms running on various -grid configurations (2 $\times$ 16, 4 $\times$ 8, 4 $\times$ 16 and 8 $\times$ 8) and using an inter-network N2 defined in the test conditions in Table~\ref{tab:01}. 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...} +\subsubsection{Simulations for two different inter-clusters network speeds\\} +In this section, the experiments compare the behavior of the algorithms running on a +speeder inter-cluster network (N2) and also on a less performant network (N1) respectively defined in the test conditions Table~\ref{tab:02}. +%\RC{Il faut définir cela avant...} +Figure~\ref{fig:02} shows that end users will reduce the execution time +for both algorithms when using a grid architecture like 4 $\times$ 16 or 8 $\times$ 8: the reduction factor is around $2$. The results depict also that when +the network speed drops down (variation of 12.5\%), the difference between the two Multisplitting algorithms execution times can reach more than 25\%. -\begin{figure} [ht!] - \begin{center} - \includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf} - \end{center} - \caption{Various grid configurations with the input matrix size $N_{x}=150$ and $N_{x}=170$\RC{idem} -\AG{Utiliser le point comme séparateur décimal et non la virgule. Idem dans les autres figures.}} - \label{fig:01} +\begin{figure}[t] +\centering +\includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf} +\caption{Various grid configurations with networks $N1$ vs. $N2$} +\label{fig:02} \end{figure} -Secondly, the execution times between the two algorithms is significant with different -grid architectures, even with the same number of processors (for example, 2 $\times$ 16 -and 4 $\times$ 8). We can observ the sensitivity of the Krylov multisplitting method -(compared with the classical GMRES) when scaling up the number of the processors -in the grid: in average, the reduction of the execution time for GMRES (resp. Multisplitting) algorithm is around $40\%$ (resp. around $48\%$) when running from 32 (grid 2 $\times$ 16) to 64 processors (grid 8 $\times$ 8) processors. \RC{pas très clair, c'est pas précis de dire qu'un algo perform mieux qu'un autre, selon quel critère?} -\subsubsection{Running on two different inter-clusters network speeds \\} -\begin{table} [ht!] -\begin{center} -\begin{tabular}{r c } - \hline - Grid Architecture & 2 $\times$ 16, 4 $\times$ 8\\ %\hline - Inter Networks & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\hline - - & N2 : bw=1Gbs-lat=5.10$^{-5}$ \\ - Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline - \end{tabular} -\caption{Test conditions: grid 2 $\times$ 16 and 4 $\times$ 8 with networks N1 vs N2} -\label{tab:02} -\end{center} -\end{table} -In this section, the experiments compare the behavior of the algorithms running on a -speeder inter-cluster network (N1) and also on a less performant network (N2) respectively defined in the test conditions Table~\ref{tab:02}. \RC{Il faut définir cela avant...} -Figure~\ref{fig:02} shows that end users will reduce the execution time -for both algorithms when using a grid architecture like 4 $\times$ 16 or 8 $\times$ 8: the reduction is about $2$. The results depict also that when -the network speed drops down (variation of 12.5\%), the difference between the two Multisplitting algorithms execution times can reach more than 25\%. -%\begin{wrapfigure}{l}{100mm} -\begin{figure} [ht!] -\centering -\includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf} -\caption{Grid 2 $\times$ 16 and 4 $\times$ 8 with networks N1 vs N2 -\AG{\np{8E-6}, \np{5E-6} au lieu de 8E-6, 5E-6}} -\label{fig:02} -\end{figure} -%\end{wrapfigure} -\subsubsection{Network latency impacts on performance} -\ \\ + + + + + + + + + + + + +\subsubsection{Network latency impacts on performance\\} + \begin{table} [ht!] \centering \begin{tabular}{r c } \hline Grid Architecture & 2 $\times$ 16\\ %\hline - Network & N1 : bw=1Gbs \\ %\hline + \multirow{2}{*}{Inter Network N1} & $bw$=1Gbs, \\ %\hline + & $lat$= From 8$\times$10$^{-6}$ to $6.10^{-5}$ second \\ Input matrix size & $N_{x} \times N_{y} \times N_{z} = 150 \times 150 \times 150$\\ \hline \end{tabular} \caption{Test conditions: network latency impacts} \label{tab:03} \end{table} - - -\begin{figure} [ht!] +\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} +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 $6.10^{-5}$ second. -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. In addition, it appears that the -Krylov multisplitting method tolerates more the network latency variation with a -less rate increase of the execution time.\RC{Les 2 précédentes phrases me - semblent en contradiction....} Consequently, in the worst case ($lat=6.10^{-5 -}$), the execution time for GMRES is almost the double than the time of the -Krylov multisplitting, even though, the performance was on the same order of -magnitude with a latency of $8.10^{-6}$. -\subsubsection{Network bandwidth impacts on performance} -\ \\ +\subsubsection{Network bandwidth impacts on performance\\} + \begin{table} [ht!] \centering \begin{tabular}{r c } \hline Grid Architecture & 2 $\times$ 16\\ %\hline - Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline +\multirow{2}{*}{Inter Network N1} & $bw$=From 1Gbs to 10 Gbs \\ %\hline + & $lat$= 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}} +\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} -\begin{figure} [ht!] +\begin{figure} [htbp] \centering \includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf} -\caption{Network bandwith impacts on execution time -\AG{``Execution time'' avec un 't' minuscule}. Idem autres figures.} +\caption{Network bandwith impacts on execution time} +%\AG{``Execution time'' avec un 't' minuscule}. Idem autres figures.} +%\RCE{Corrige} \label{fig:04} \end{figure} @@ -696,55 +674,54 @@ Figure~\ref{fig:04}). However, in this case, the Krylov multisplitting method presents a better performance in the considered bandwidth interval with a gain of $40\%$ which is only around $24\%$ for the classical GMRES. -\subsubsection{Input matrix size impacts on performance} -\ \\ +\subsubsection{Input matrix size impacts on performance\\} + \begin{table} [ht!] \centering \begin{tabular}{r c } \hline Grid Architecture & 4 $\times$ 8\\ %\hline - Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ - Input matrix size & $N_{x}$ = From 40 to 200\\ \hline + Inter Network & $bw$=1Gbs - $lat$=5.10$^{-5}$ \\ + 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} \end{table} -\begin{figure} [ht!] +\begin{figure} [htbp] \centering \includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf} \caption{Problem size impacts on execution time} \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 drastic increase ($10$ times) of the number of iterations needed to - reach the convergence for the classical GMRES algorithm 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} -\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 size scale up. It should be noticed that the same test has been done with the -grid 2 $\times$ 16 leading to the same conclusion. +grid 4 $\times$ 8 leading to the same conclusion. -\subsubsection{CPU Power impacts on performance} +\subsubsection{CPU Power impacts on performance\\} -\begin{table} [ht!] + +\begin{table} [htbp] \centering \begin{tabular}{r c } \hline Grid architecture & 2 $\times$ 16\\ %\hline - Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline - Input matrix size & $N_{x} = 150 \times 150 \times 150$\\ \hline + Inter Network & N2 : $bw$=1Gbs - $lat$=5.10$^{-5}$ \\ %\hline + Input matrix size & $N_{x} = 150 \times 150 \times 150$\\ + CPU Power & From 3 to 19 GFlops \\ \hline \end{tabular} \caption{Test conditions: CPU Power impacts} \label{tab:06} @@ -789,17 +766,19 @@ synchronization with the other processors. Thus, the asynchronous may theoretically reduce the overall execution time and can improve the algorithm performance. -\RC{la phrase suivante est bizarre, je ne comprends pas pourquoi elle vient ici} -In this section, Simgrid simulator tool has been successfully used to show -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. +In this section, the Simgrid simulator is used to compare the behavior of the +multisplitting in asynchronous mode with GMRES in synchronous mode. Several +benchmarks have been performed with various combination of the grid resources +(CPU, Network, input matrix size, \ldots ). The test conditions are summarized +in Table~\ref{tab:07}. In order to compare the execution times, this table +reports the relative gain between both algorithms. It is defined by the ratio +between the execution time of GMRES and the execution time of the +multisplitting. The ration is greater than one because the asynchronous +multisplitting version is faster than GMRES. -The test conditions are summarized in the table~\ref{tab:07}: \\ -\begin{table} [ht!] +\begin{table} [htbp] \centering \begin{tabular}{r c } \hline @@ -849,7 +828,7 @@ geographically distant clusters through the internet. power (GFlops) & 1 & 1 & 1 & 1.5 & 1.5 & 1.5 & 1.5 & 1 & 1.5 & 1.5 \\ \hline - size (N) + size ($N^3$) & 62 & 62 & 62 & 100 & 100 & 110 & 120 & 130 & 140 & 150 \\ \hline Precision