From c6459f3420747227cfe477c0e22f08499ee3e4de Mon Sep 17 00:00:00 2001 From: David Laiymani Date: Wed, 6 May 2015 16:36:08 +0200 Subject: [PATCH] =?utf8?q?DL=20:=20exp=C3=A9=20encore?= MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit --- paper.tex | 34 ++++++++++++++++++++-------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/paper.tex b/paper.tex index c198158..0dc584b 100644 --- a/paper.tex +++ b/paper.tex @@ -45,6 +45,8 @@ \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace} \newcommand{\RCE}[2][inline]{% \todo[color=yellow!10,#1]{\sffamily\textbf{RCE:} #2}\xspace} +\newcommand{\DL}[2][inline]{% + \todo[color=pink!10,#1]{\sffamily\textbf{DL:} #2}\xspace} \algnewcommand\algorithmicinput{\textbf{Input:}} \algnewcommand\Input{\item[\algorithmicinput]} @@ -620,7 +622,7 @@ The results of increasing the network bandwidth show the improvement of the performance for both algorithms by reducing the execution time (see 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 classical GMRES. +of $40\%$ which is only around $24\%$ for the classical GMRES. \subsubsection{Input matrix size impacts on performance} \ \\ @@ -632,27 +634,27 @@ of 40\% which is only around 24\% for classical GMRES. Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ Input matrix size & N$_{x}$ = From 40 to 200\\ \hline \end{tabular} -\caption{Input matrix size impact} +\caption{Input matrix size impacts} \end{figure} \begin{figure} [ht!] \centering \includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf} -\caption{Problem size impact on execution time} +\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 +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 (300 times) \RC{Je ne vois pas cela sur la figure} + \item the drastic increase ($300$ times) \RC{Je ne vois pas cela sur la figure} of the number of iterations needed to reach the convergence for the classical -GMRES algorithm when the matrix size go beyond N$_{x}$=150; -\item the classical GMRES execution time is almost the double for N$_{x}$=140 +GMRES algorithm when the matrix size go beyond $N_{x}=150$; +\item the classical GMRES execution time is almost the double for $N_{x}=140$ compared with the Krylov multisplitting method. \end{enumerate} @@ -661,7 +663,7 @@ 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 2x16 leading to the same conclusion. -\subsubsection{CPU Power impact on performance} +\subsubsection{CPU Power impacts on performance} \begin{figure} [ht!] \centering @@ -671,22 +673,26 @@ grid 2x16 leading to the same conclusion. Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline \end{tabular} -\caption{CPU Power impact} +\caption{CPU Power impacts} \end{figure} \begin{figure} [ht!] \centering \includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf} -\caption{CPU Power impact on execution time} +\caption{CPU Power impacts on execution time} \label{fig:06} \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 Figure~\ref{fig:06} confirm the -performance gain, around 95\% for both of the two methods, after adding more +from $1$ to $19$ GFlops. The outputs depicted in Figure~\ref{fig:06} confirm the +performance gain, around $95\%$ for both of the two methods, after adding more powerful CPU. +\DL{il faut une conclusion sur ces tests : ils confirment les résultats déjà +obtenus en grandeur réelle. Donc c'est une aide précieuse pour les dev. Pas +besoin de déployer sur une archi réelle} + \subsection{Comparing GMRES in native synchronous mode and the multisplitting algorithm in asynchronous mode} The previous paragraphs put in evidence the interests to simulate the behavior -- 2.39.5