From: RCE Date: Thu, 7 May 2015 22:24:12 +0000 (+0200) Subject: RCE : Correction parrtie expe X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/2f78f080350308e2f46d8eff8d66a8e127fee583?ds=inline;hp=-c RCE : Correction parrtie expe --- 2f78f080350308e2f46d8eff8d66a8e127fee583 diff --git a/paper.tex b/paper.tex index d5a458c..f9434f9 100644 --- a/paper.tex +++ b/paper.tex @@ -555,12 +555,12 @@ architectures and scaling up the input matrix size} \begin{center} \begin{tabular}{r c } \hline - Grid Architecture & 2x16, 4x8, 4x16 and 8x8\\ %\hline - Network & N2 : bw=1Gbits/s - lat=5.10$^{-5}$ \\ %\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 matix size N$_{x}$=150 or N$_{x}$=170 \RC{N2 n'est pas défini..}\RC{Nx est défini, Ny? Nz?} +\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.}} \label{tab:01} \end{center} @@ -571,13 +571,13 @@ architectures and scaling up the input matrix size} In this section, we analyze the performance of algorithms running on various -grid configurations (2x16, 4x8, 4x16 and 8x8). First, the results in Figure~\ref{fig:01} +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...} +%\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...} \begin{figure} [ht!] @@ -590,12 +590,11 @@ multisplitting method. \end{figure} -The execution times between the two algorithms is significant with different -grid architectures, even with the same number of processors (for example, 2x16 -and 4x8). We can observ the low sensitivity of the Krylov multisplitting method +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 GMRES (resp. Multisplitting) algorithm performs -$40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors. \RC{pas très clair, c'est pas précis de dire qu'un algo perform mieux qu'un autre, selon quel critère?} +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 \\} @@ -603,20 +602,20 @@ $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors. \RC \begin{center} \begin{tabular}{r c } \hline - Grid Architecture & 2x16, 4x8\\ %\hline - Network & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\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 2x16 and 4x8 with networks N1 vs N2} +\caption{Test conditions: grid 2 $\times$ 16 and 4 $\times$ 8 with networks N1 vs N2} \label{tab:02} \end{center} \end{table} -These experiments compare the behavior of the algorithms running first on a -speed inter-cluster network (N1) and also on a less performant network (N2). \RC{Il faut définir cela avant...} +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 4x16 or 8x8: the reduction is about $2$. The results depict also that when +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\%. @@ -625,7 +624,7 @@ the network speed drops down (variation of 12.5\%), the difference between t \begin{figure} [ht!] \centering \includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf} -\caption{Grid 2x16 and 4x8 with networks N1 vs N2 +\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} @@ -638,7 +637,7 @@ the network speed drops down (variation of 12.5\%), the difference between t \centering \begin{tabular}{r c } \hline - Grid Architecture & 2x16\\ %\hline + Grid Architecture & 2 $\times$ 16\\ %\hline Network & N1 : bw=1Gbs \\ %\hline Input matrix size & $N_{x} \times N_{y} \times N_{z} = 150 \times 150 \times 150$\\ \hline \end{tabular} @@ -674,7 +673,7 @@ magnitude with a latency of $8.10^{-6}$. \centering \begin{tabular}{r c } \hline - Grid Architecture & 2x16\\ %\hline + Grid Architecture & 2 $\times$ 16\\ %\hline Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline \\ \end{tabular} @@ -703,7 +702,7 @@ of $40\%$ which is only around $24\%$ for the classical GMRES. \centering \begin{tabular}{r c } \hline - Grid Architecture & 4x8\\ %\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 \end{tabular} @@ -735,7 +734,7 @@ But the interesting results are: 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 2x16 leading to the same conclusion. +grid 2 $\times$ 16 leading to the same conclusion. \subsubsection{CPU Power impacts on performance} @@ -743,7 +742,7 @@ grid 2x16 leading to the same conclusion. \centering \begin{tabular}{r c } \hline - Grid architecture & 2x16\\ %\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 \end{tabular} @@ -804,7 +803,7 @@ The test conditions are summarized in the table~\ref{tab:07}: \\ \centering \begin{tabular}{r c } \hline - Grid Architecture & 2x50 totaling 100 processors\\ %\hline + Grid Architecture & 2 $\times$ 50 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$^{-2}$\\