X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/49ee5215a4f7de973138cd830c4b5506e6497a34..0ff5badea3e5156e9795cf5724f3dffed49ca7b5:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index fab2e82..523716f 100644
--- a/paper.tex
+++ b/paper.tex
@@ -541,10 +541,9 @@ In the scope  of this paper, our  first objective is to analyze  when the Krylov
 two-stage method has  better  performance  than   the  classical  GMRES method. With a synchronous  iterative method, better performance means a
 smaller number of iterations and execution time 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.
+grid  parameters values, the simulator will confirm Multisplitting method  better performance compared to classical GMRES, particularly on poor and slow networks.
 \LZK{Pas du tout claire la dernière phrase (For a systematic...)!!}
+\RCE { Reformule autrement}
 
 In what follows, we will present the test conditions, the output results and our comments.\\
 
@@ -558,12 +557,14 @@ In what follows, we will present the test conditions, the output results and our
 \begin{tabular}{ll }
  \hline
  Grid architecture & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\ %\hline
- Network           & N1 : $bw$=1Gbits/s, $lat$=5$\times$10$^{-5}$ \\ %\hline
+ \multirow{2}{*}{Network} & Inter (N2): $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ %\hline
+                          & Intra (N1): $bw$=10Gbs, $lat$=8$\times$10$^{-6}$ \\
  \multirow{2}{*}{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 matrix sizes 150$^3$ or 170$^3$}
 \LZK{Ce sont les caractéristiques du réseau intra ou inter clusters? Ce n'est pas précisé...}
+\RCE{oui c est precise}
 \label{tab:01}
 \end{center}
 \end{table}
@@ -576,6 +577,7 @@ In this section, we analyze the simulations conducted on various grid configurat
 %% 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...}
+\RCE{Corrige}
 
 \begin{figure} [ht!]
   \begin{center}
@@ -584,17 +586,19 @@ In this section, we analyze the simulations conducted on various grid configurat
   \caption{Various grid configurations with the matrix sizes 150$^3$ and 170$^3$
 \AG{Utiliser le point comme séparateur décimal et non la virgule.  Idem dans les autres figures.}}
 \LZK{Pour quelle taille du problème sont calculés les nombres d'itérations? Que représente le 2 Clusters x 16 Nodes with Nx=150 and Nx=170 en haut de la figure?}
+\RCE {Corrige}
   \label{fig:01}
 \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  observe  the low  sensitivity  of  the Krylov multisplitting  method
+grid architectures, even  with the same number of processors  (for example, 2 $\times$ 16
+and  4 $\times  8$). We  can  observe  a better  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. 
+$40\%$ better (resp. $48\%$) when running from 32 (grid 2 $\times$ 16) to 64 processors/cores (grid 8 $\times$ 8). Note that even with a grid 8 $\times$ 8 having the maximum number of clusters, the execution time of the multisplitting method is in average 32\% less compared to GMRES. 
 \RC{pas très clair, c'est pas précis de dire qu'un algo perform mieux qu'un autre, selon quel critère?}
 \LZK{A revoir toute cette analyse... Le multi est plus performant que GMRES. Les temps d'exécution de multi sont sensibles au nombre de CLUSTERS. Il est moins performant pour un nombre grand de cluster. Avez vous d'autres remarques?}
+\RCE{Remarquez que meme avec une grille 8x8, le multi est toujours plus performant}
 
 \subsubsection{Simulations for two different inter-clusters network speeds \\}
 
@@ -603,7 +607,7 @@ $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
 \begin{tabular}{ll}
  \hline
  Grid architecture        & 2$\times$16, 4$\times$8\\ %\hline
- \multirow{2}{*}{Network} & N1: $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ %\hline
+ \multirow{2}{*}{Inter Network} & N1: $bw$=1Gbs, $lat$=5$\times$10$^{-5}$ \\ %\hline
                           & N2: $bw$=10Gbs, $lat$=8$\times$10$^{-6}$ \\
  Matrix size              & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline
  \end{tabular}
@@ -615,7 +619,7 @@ $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
 In this section, the experiments  compare the  behavior of  the algorithms  running on a
 speeder inter-cluster  network (N2) and  also on  a less performant  network (N1) 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  4 $\times$ 16 or  8 $\times$ 8: 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 factor is around $2$. 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\%.
 
 
@@ -624,8 +628,9 @@ 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 2 $\times$ 16 and 4 $\times$ 8 with networks N1 vs N2
+\caption{Various grid configurations with networks N1 vs N2
 \AG{\np{8E-6}, \np{5E-6} au lieu de 8E-6, 5E-6}}
+\RCE{Corrige}
 \label{fig:02}
 \end{figure}
 %\end{wrapfigure}
@@ -638,15 +643,14 @@ the  network speed  drops down (variation of 12.5\%), the  difference between  t
 \begin{tabular}{r c }
  \hline
  Grid Architecture & 2 $\times$ 16\\ %\hline
- Network & N1 : bw=1Gbs \\ %\hline
+ \multirow{2}{*}{Inter Network N1} & $bw$=1Gbs, \\ %\hline
+                          & $lat$= From 8$\times$10$^{-6}$ to  $6.10^{-5}$ second \\
  Input matrix size & $N_{x} \times N_{y} \times N_{z} = 150 \times 150 \times 150$\\ \hline
  \end{tabular}
 \caption{Test conditions: network latency impacts}
 \label{tab:03}
 \end{table}
 
-
-
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf}
@@ -655,17 +659,13 @@ the  network speed  drops down (variation of 12.5\%), the  difference between  t
 \label{fig:03}
 \end{figure}
 
-
 According to  the results of  Figure~\ref{fig:03}, a degradation of  the network
 latency from  $8.10^{-6}$ to  $6.10^{-5}$ implies an  absolute time  increase of
 more  than $75\%$  (resp.  $82\%$)  of the  execution  for  the classical  GMRES
-(resp.  Krylov multisplitting)  algorithm.   In addition,  it  appears that  the
-Krylov multisplitting method tolerates more the network latency variation with a
-less  rate increase  of  the  execution time.\RC{Les  2  précédentes phrases  me
-  semblent en contradiction....}  Consequently, in the worst case ($lat=6.10^{-5
-}$), the  execution time for  GMRES is  almost the double  than the time  of the
-Krylov multisplitting,  even though, the  performance was  on the same  order of
-magnitude with a latency of $8.10^{-6}$.
+(resp.  Krylov multisplitting)  algorithm which means that the GMRES seems tolerate more the network latency variation with a less  rate increase  of  the  execution time. However, the execution time factor between the two algorithms varies from 2.2 to 1.5 times with a network latency decreasing from $8.10^{-6}$ to  $6.10^{-5}$.
+
+\RC{Les  2  précédentes phrases  me  semblent en contradiction....}  
+\RCE{Reformule}
 
 \subsubsection{Network bandwidth impacts on performance}
 \ \\
@@ -674,10 +674,12 @@ magnitude with a latency of $8.10^{-6}$.
 \begin{tabular}{r c }
  \hline
  Grid Architecture & 2 $\times$ 16\\ %\hline
- Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
+\multirow{2}{*}{Inter Network N1} & $bw$=From 1Gbs to 10 Gbs \\ %\hline
+                          & $lat$= 5.10$^{-5}$ second \\
  Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline \\
  \end{tabular}
 \caption{Test conditions: Network bandwidth impacts\RC{Qu'est ce qui varie ici? Il n'y a pas de variation dans le tableau}}
+\RCE{C est le bw}
 \label{tab:04}
 \end{table}
 
@@ -687,6 +689,7 @@ magnitude with a latency of $8.10^{-6}$.
 \includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf}
 \caption{Network bandwith impacts on execution time
 \AG{``Execution time'' avec un 't' minuscule}. Idem autres figures.}
+\RCE{Corrige}
 \label{fig:04}
 \end{figure}
 
@@ -703,8 +706,8 @@ of $40\%$ which is only around $24\%$ for the classical GMRES.
 \begin{tabular}{r c }
  \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
+ Inter Network & $bw$=1Gbs - $lat$=5.10$^{-5}$ \\
+ Input matrix size & $N_{x} \times N_{y} \times N_{z}$ = From 40$^{3}$ to 200$^{3}$\\ \hline
  \end{tabular}
 \caption{Test conditions: Input matrix size impacts}
 \label{tab:05}
@@ -724,9 +727,11 @@ In these experiments, the input matrix size  has been set from $N_{x} = N_{y}
 time for  both algorithms increases when  the input matrix size  also increases.
 But the interesting results are:
 \begin{enumerate}
-  \item the drastic increase ($10$ times)  of the number of iterations needed to
-    reach the convergence for the classical GMRES algorithm when the matrix size
+  \item the important increase ($10$ times)  of the number of iterations needed to
+    reach the convergence for the classical GMRES algorithm particularly, when the matrix size
     go beyond $N_{x}=150$; \RC{C'est toujours pas clair... ok le nommbre d'itérations est 10 fois plus long mais la suite de la phrase ne veut rien dire}
+    \RCE{Le nombre d'iterations augmente de 10 fois, cela surtout a partir de N=150}
+    
 \item the  classical GMRES execution time  is almost the double  for $N_{x}=140$
   compared with the Krylov multisplitting method.
 \end{enumerate}
@@ -743,8 +748,9 @@ grid 2 $\times$ 16 leading to the same conclusion.
 \begin{tabular}{r c }
  \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
+ Inter Network & N2 : $bw$=1Gbs - $lat$=5.10$^{-5}$ \\ %\hline
+ Input matrix size & $N_{x} = 150 \times 150 \times 150$\\ 
+ CPU Power & From 3 to 19 GFlops \\ \hline
  \end{tabular}
 \caption{Test conditions: CPU Power impacts}
 \label{tab:06}
@@ -790,6 +796,7 @@ theoretically reduce  the overall execution  time and can improve  the algorithm
 performance.
 
 \RC{la phrase suivante est bizarre, je ne comprends pas pourquoi elle vient ici}
+\RCE{C est la description du dernier test sync/async avec l'introduction de la notion de relative gain}
 In this section, Simgrid simulator tool has been successfully used to show
 the efficiency of  the multisplitting in asynchronous mode and  to find the best
 combination of the grid resources (CPU,  Network, input matrix size, \ldots ) to