From: couturie Date: Sun, 26 Apr 2015 20:02:21 +0000 (+0200) Subject: Merge branch 'master' of ssh://bilbo.iut-bm.univ-fcomte.fr/rce2015 X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/f2509eae84a623fa2e4d8ce48287c665efb02f9d?ds=sidebyside;hp=-c Merge branch 'master' of ssh://bilbo.iut-bm.univ-fcomte.fr/rce2015 --- f2509eae84a623fa2e4d8ce48287c665efb02f9d diff --combined paper.tex index 8e5c695,6dc375d..f800225 --- a/paper.tex +++ b/paper.tex @@@ -92,18 -92,14 +92,18 @@@ %% Lilia Ziane Khodja: Department of Aerospace \& Mechanical Engineering\\ Non Linear Computational Mechanics\\ University of Liege\\ Liege, Belgium. Email: l.zianekhodja@ulg.ac.be \begin{abstract} -ABSTRACT + The behavior of multicore applications is always a challenge to predict, especially with a new architecture for which no experiment has been performed. With some applications, it is difficult, if not impossible, to build accurate performance models. That is why another solution is to use a simulation tools that allows us to change many parameters of the architecture (network bandwidth, latency, number of processors) and to simulate the execution of such applications. + + In this paper, we focus our attention on two parallel iterative algorithms: one with synchronoous iterations and another one with asynchronous iterations. + + \end{abstract} \keywords{Algorithm; distributed; iterative; asynchronous; simulation; simgrid; performance} \maketitle -\section{Introduction} +\section{Introduction} \section{The asynchronous iteration model} @@@ -249,7 -245,7 +249,7 @@@ paper, (2) using the multisplitting met 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 @@@ -290,7 -286,7 +290,7 @@@ Another important factor impacting 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 @@@ -323,7 -319,7 +323,7 @@@ before reaching the convergence. For a 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 $[$12$]$. + on the chosen class of algorithm. The following paragraphs present the test conditions, the output results and our comments. @@@ -342,11 -338,12 +342,12 @@@ architecture scaling up the input matri Input matrix size & N$_{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} @@@ -354,10 -351,10 +355,10 @@@ The results in figure 1 show the non-va 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} @@@ -371,7 -368,7 +372,7 @@@ experiment concludes the low sensitivit (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} @@@ -382,16 -379,16 +383,16 @@@ - & 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} @@@ -405,7 -402,7 +406,7 @@@ performance was increased in a factor o 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} @@@ -415,14 -412,16 +416,16 @@@ 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} @@@ -438,7 -437,7 +441,7 @@@ a less rate increase. Consequently, in 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} @@@ -448,13 -447,15 +451,15 @@@ 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} @@@ -467,7 -468,7 +472,7 @@@ algorithms. However, and again in this 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} @@@ -477,13 -478,14 +482,14 @@@ 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} @@@ -502,7 -504,7 +508,7 @@@ the best and the optimal targeted envir 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} @@@ -512,13 -514,14 +518,14 @@@ 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} @@@ -558,7 -561,7 +565,7 @@@ best combination of the grid resources classical GMRES time. - The test conditions are summarized in the table below : + The test conditions are summarized in the table below : \\ % environment \begin{footnotesize} @@@ -569,7 -572,7 +576,7 @@@ 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}