X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/7f0014c901a78db6eabc4e705b0fb6f0dd7b681e..c6ed6c1a3600ab1fed77636cbae31b12198c6485:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index cdbc6e7..eaaa8fb 100644
--- a/paper.tex
+++ b/paper.tex
@@ -92,14 +92,27 @@
 %% 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 tool which allows us to change many parameters of the architecture (network bandwidth, latency, number of processors) and to simulate the execution of such applications. We have decided to use SimGrid as it enables to benchmark MPI applications.
+
+In this paper, we focus our attention on two parallel iterative algorithms based
+on the  Multisplitting algorithm  and we  compare them  to the  GMRES algorithm.
+These algorithms  are used to  solve libear  systems. Two different  variantsof the Multisplitting are
+studied: one  using synchronoous  iterations and  another one  with asynchronous
+iterations. For each algorithm we have  tested different parameters to see their
+influence.  We strongly  recommend people  interested  by investing  into a  new
+expensive  hardware  architecture  to   benchmark  their  applications  using  a
+simulation tool before.
+
+  
+
+  
 \end{abstract}
 
 \keywords{Algorithm; distributed; iterative; asynchronous; simulation; simgrid; performance}
 
 \maketitle
 
-\section{Introduction}
+\section{Introduction} 
 
 \section{The asynchronous iteration model}
 
@@ -245,7 +258,7 @@ 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 +299,7 @@ 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 +332,7 @@ 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 +351,12 @@ 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 +364,10 @@ 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 +381,7 @@ 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 +392,16 @@ 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 \\
 
-%Table 2 : Clusters x Nodes - Networks N1 x N2
-%\RCE{idem pour tous les tableaux de donnees}
+ \end{footnotesize}
 
 
-%\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 +415,7 @@ 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 +425,16 @@ 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 +450,7 @@ 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 +460,15 @@ 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 +481,7 @@ 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 +491,14 @@ 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 +517,7 @@ 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 +527,14 @@ 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 +574,7 @@ 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 +585,7 @@ 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}