From: RCE <cramamonjisoa@bilbo.iut-bm.univ-fcomte.fr>
Date: Tue, 28 Apr 2015 11:52:39 +0000 (+0200)
Subject: RCE : Quelques corrections
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/a91b3c5e161102f4d9c2bb345a28eefc71167ada?ds=sidebyside

RCE : Quelques corrections
---

diff --git a/paper.tex b/paper.tex
index ce1305d..73c98e4 100644
--- a/paper.tex
+++ b/paper.tex
@@ -434,7 +434,7 @@ Table 3 : Network latency impact \\
 \end{figure}
 
 
-According the results in table and figure 5, degradation of the network 
+According the results in figure 5, degradation of the network 
 latency from 8.10$^{-6}$ to 6.10$^{-5}$ implies an absolute time 
 increase more than 75\% (resp. 82\%) of the execution for the classical 
 GMRES (resp. multisplitting) algorithm. In addition, it appears that the 
@@ -451,10 +451,9 @@ of magnitude with a latency of 8.10$^{-6}$.
 \begin{tabular}{r c }
  \hline  
  Grid & 2x16\\ %\hline
- Network & N1 : bw=1Gbs - lat=5E-05 \\ %\hline
- Input matrix size & N$_{x}$ =150 x 150 x 150\\ \hline
+ Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline \\
  \end{tabular}
-
 Table 4 : Network bandwidth impact \\
 
 \end{footnotesize}
@@ -471,7 +470,7 @@ Table 4 : Network bandwidth impact \\
 
 The results of increasing the network bandwidth depict the improvement 
 of the performance by reducing the execution time for both of the two 
-algorithms. However, and again in this case, the multisplitting method 
+algorithms (Figure 6). 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.
 
@@ -482,8 +481,8 @@ a gain of 40\% which is only around 24\% for classical GMRES.
 \begin{tabular}{r c }
  \hline  
  Grid & 4x8\\ %\hline
- Network & N2 : bw=1Gbs - lat=5E-05 \\ %\hline
- Input matrix size & N$_{x}$ = From 40 to 200\\ \hline
+ Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ = From 40 to 200\\ \hline \\
  \end{tabular}
 Table 5 : Input matrix size impact\\
 
@@ -498,14 +497,14 @@ Table 5 : Input matrix size impact\\
 \end{figure}
 
 In this experimentation, the input matrix size has been set from 
-Nx=Ny=Nz=40 to 200 side elements that is from 40$^{3}$ = 64.000 to 
-200$^{3}$ = 8.000.000 points. Obviously, as shown in the figure 5, 
-the execution time for the algorithms convergence increases with the 
-input matrix size. But the interesting result here direct on (i) the 
+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 the figure 7, 
+the execution time for the two algorithms convergence increases with the 
+input matrix size. But the interesting results here direct on (i) the 
 drastic increase (300 times) of the number of iterations needed before 
 the convergence for the classical GMRES algorithm when the matrix size 
-go beyond Nx=150; (ii) the classical GMRES execution time also almost 
-the double from Nx=140 compared with the convergence time of the 
+go beyond N$_{x}$=150; (ii) the classical GMRES execution time also almost 
+the double from N$_{x}$=140 compared with the convergence time of the 
 multisplitting method. 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. Note that the