From: RCE Date: Sun, 26 Apr 2015 19:59:29 +0000 (+0200) Subject: RCE : Modification de la taille des graphiques X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/72903632bc99cb0f291e182f94b261cfbf7b0ec8?hp=-c RCE : Modification de la taille des graphiques Quelques corrections --- 72903632bc99cb0f291e182f94b261cfbf7b0ec8 diff --git a/paper.tex b/paper.tex index 94c5125..6dc375d 100644 --- a/paper.tex +++ b/paper.tex @@ -245,7 +245,7 @@ paper, (2) using the multisplitting method alias Algo-2 and (3) an enhanced version of the multisplitting method as Algo-3. In addition, SIMGRID simulator has been chosen to simulate the behaviors of the distributed applications. SIMGRID is running on the Mesocentre -datacenter in Franche-Comte University $[$10$]$ but also in a virtual +datacenter in Franche-Comte University but also in a virtual machine on a laptop. \textbf{Step 3} : Fix the criteria which will be used for the future @@ -286,7 +286,7 @@ Another important factor impacting the overall performance of the application is the network configuration. Two main network parameters can modify drastically the program output results : (i) the network bandwidth (bw=bits/s) also known as "the data-carrying capacity" -$[$13$]$ of the network is defined as the maximum of data that can pass +of the network is defined as the maximum of data that can pass from one point to another in a unit of time. (ii) the network latency (lat : microsecond) defined as the delay from the start time to send the data from a source and the final time the destination have finished to @@ -319,7 +319,7 @@ 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 $[$12$]$. +on the chosen class of algorithm. The following paragraphs present the test conditions, the output results and our comments. @@ -338,11 +338,12 @@ architecture scaling up the input matrix size} Input matrix size & N$_{x}$ =150 x 150 x 150 and\\ %\hline - & N$_{x}$ =170 x 170 x 170 \\ \hline \end{tabular} -\end{footnotesize} +Table 1 : Clusters x Nodes with NX=150 or NX=170 \\ +\end{footnotesize} - Table 1 : Clusters x Nodes with NX=150 or NX=170 + %\RCE{J'ai voulu mettre les tableaux des données mais je pense que c'est inutile et ça va surcharger} @@ -350,10 +351,10 @@ The results in figure 1 show 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. -%\begin{wrapfigure}{l}{60mm} +%\begin{wrapfigure}{l}{100mm} \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{cluster_x_nodes_nx_150_and_nx_170.pdf} +\includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf} \caption{Cluster x Nodes NX=150 and NX=170} %\label{overflow}} \end{figure} @@ -367,7 +368,7 @@ experiment concludes the low sensitivity of the multisplitting method (compared with the classical GMRES) when scaling up to higher input matrix size. -\textit{3.b Running on various computational grid architecture} +\textit{\\3.b Running on various computational grid architecture\\} % environment \begin{footnotesize} @@ -378,16 +379,16 @@ matrix size. - & N2 : bw=1Gbs-lat=5E-05 \\ Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline \\ \end{tabular} -\end{footnotesize} +Table 2 : Clusters x Nodes - Networks N1 x N2 \\ + + \end{footnotesize} -%Table 2 : Clusters x Nodes - Networks N1 x N2 -%\RCE{idem pour tous les tableaux de donnees} -%\begin{wrapfigure}{l}{60mm} +%\begin{wrapfigure}{l}{100mm} \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{cluster_x_nodes_n1_x_n2.pdf} +\includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf} \caption{Cluster x Nodes N1 x N2} %\label{overflow}} \end{figure} @@ -401,7 +402,7 @@ performance was increased in a factor of 2. The results depict also that when the network speed drops down, the difference between the execution times can reach more than 25\%. -\textit{3.c Network latency impacts on performance} +\textit{\\3.c Network latency impacts on performance\\} % environment \begin{footnotesize} @@ -411,14 +412,16 @@ times can reach more than 25\%. Network & N1 : bw=1Gbs \\ %\hline Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline\\ \end{tabular} + +Table 3 : Network latency impact \\ + \end{footnotesize} -Table 3 : Network latency impact \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{network_latency_impact_on_execution_time.pdf} +\includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf} \caption{Network latency impact on execution time} %\label{overflow}} \end{figure} @@ -434,7 +437,7 @@ a less rate increase. Consequently, in the worst case (lat=6.10$^{-5 the multisplitting, even though, the performance was on the same order of magnitude with a latency of 8.10$^{-6}$. -\textit{3.d Network bandwidth impacts on performance} +\textit{\\3.d Network bandwidth impacts on performance\\} % environment \begin{footnotesize} @@ -444,13 +447,15 @@ of magnitude with a latency of 8.10$^{-6}$. Network & N1 : bw=1Gbs - lat=5E-05 \\ %\hline Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline \end{tabular} + +Table 4 : Network bandwidth impact \\ + \end{footnotesize} -Table 4 : Network bandwidth impact \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{network_bandwith_impact_on_execution_time.pdf} +\includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf} \caption{Network bandwith impact on execution time} %\label{overflow} \end{figure} @@ -463,7 +468,7 @@ algorithms. However, and again in this case, the multisplitting method presents a better performance in the considered bandwidth interval with a gain of 40\% which is only around 24\% for classical GMRES. -\textit{3.e Input matrix size impacts on performance} +\textit{\\3.e Input matrix size impacts on performance\\} % environment \begin{footnotesize} @@ -473,13 +478,14 @@ a gain of 40\% which is only around 24\% for classical GMRES. Network & N2 : bw=1Gbs - lat=5E-05 \\ %\hline Input matrix size & N$_{x}$ = From 40 to 200\\ \hline \end{tabular} +Table 5 : Input matrix size impact\\ + \end{footnotesize} -Table 5 : Input matrix size impact \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{pb_size_impact_on_execution_time.pdf} +\includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf} \caption{Pb size impact on execution time} %\label{overflow}} \end{figure} @@ -498,7 +504,7 @@ the best and the optimal targeted environment for the application deployment when focusing on the problem size scale up. Note that the same test has been done with the grid 2x16 getting the same conclusion. -\textit{3.f CPU Power impact on performance} +\textit{\\3.f CPU Power impact on performance\\} % environment \begin{footnotesize} @@ -508,13 +514,14 @@ same test has been done with the grid 2x16 getting the same conclusion. Network & N2 : bw=1Gbs - lat=5E-05 \\ %\hline Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline \end{tabular} +Table 6 : CPU Power impact \\ + \end{footnotesize} -Table 6 : CPU Power impact \begin{figure} [ht!] \centering -\includegraphics[width=60mm]{cpu_power_impact_on_execution_time.pdf} +\includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf} \caption{CPU Power impact on execution time} %\label{overflow}} \end{figure} @@ -554,7 +561,7 @@ best combination of the grid resources (CPU, Network, input matrix size, classical GMRES time. -The test conditions are summarized in the table below : +The test conditions are summarized in the table below : \\ % environment \begin{footnotesize} @@ -565,7 +572,7 @@ The test conditions are summarized in the table below : Intra-Network & bw=1.25 Gbits - lat=5E-05 \\ %\hline Inter-Network & bw=5 Mbits - lat=2E-02\\ Input matrix size & N$_{x}$ = From 62 to 150\\ %\hline - Residual error precision: 10$^{-5}$ to 10$^{-9}$\\ \hline + Residual error precision: 10$^{-5}$ to 10$^{-9}$\\ \hline \\ \end{tabular} \end{footnotesize}