X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/1202c4ef616b826822fb0f5997da04e599a3c20f..c6459f3420747227cfe477c0e22f08499ee3e4de:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index 1f9b49c..0dc584b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -45,6 +45,8 @@
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
 \newcommand{\RCE}[2][inline]{%
   \todo[color=yellow!10,#1]{\sffamily\textbf{RCE:} #2}\xspace}
+\newcommand{\DL}[2][inline]{%
+    \todo[color=pink!10,#1]{\sffamily\textbf{DL:} #2}\xspace}
 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
@@ -465,19 +467,19 @@ and  between distant  clusters.  This parameter is application dependent.
 
 In the scope  of this paper, our  first objective is to analyze  when the Krylov
 Multisplitting  method   has  better  performances  than   the  classical  GMRES
-method. With an  iterative method, better performances mean 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.
+method. With a synchronous  iterative method, better performances mean 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.
 
 The following paragraphs present the test conditions, the output results
 and our comments.\\
 
 
-\subsubsection{Execution of the the algorithms on various computational grid
-architecture and scaling up the input matrix size}
+\subsubsection{Execution of the algorithms on various computational grid
+architectures and scaling up the input matrix size}
 \ \\
 % environment
 
@@ -501,9 +503,9 @@ architecture and scaling up the input matrix size}
 
 
 In this  section, we analyze the  performences of algorithms running  on various
-grid configuration  (2x16, 4x8, 4x16  and 8x8). First,  the results in  Figure~\ref{fig:01}
-show for all grid configuration the non-variation of the number of iterations of
-classical  GMRES for  a given  input matrix  size; it  is not  the case  for the
+grid configurations  (2x16, 4x8, 4x16  and 8x8). First,  the results in  Figure~\ref{fig:01}
+show for all grid configurations 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.
 
 \RC{CE attention tu n'as pas mis de label dans tes figures, donc c'est le bordel, j'en mets mais vérifie...}
@@ -524,9 +526,9 @@ grid architectures, even  with the same number of processors  (for example, 2x16
 and  4x8). We  can  observ  the low  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\%) less when running from 2x16=32 to 8x8=64 processors.
+$40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
 
-\subsubsection{Running on two different speed cluster inter-networks}
+\subsubsection{Running on two different inter-clusters network speed}
 \ \\
 
 \begin{figure} [ht!]
@@ -536,7 +538,7 @@ in the  grid: in  average, the GMRES  (resp. Multisplitting)  algorithm performs
  Grid & 2x16, 4x8\\ %\hline
  Network & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\hline
  - & N2 : bw=1Gbs-lat=5.10$^{-5}$ \\
- Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline 
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline
  \end{tabular}
 \caption{Clusters x Nodes - Networks N1 x N2}
 \end{center}
@@ -557,9 +559,10 @@ These experiments  compare the  behavior of  the algorithms  running first  on a
 speed inter-cluster  network (N1) and  also on  a less performant  network (N2).
 Figure~\ref{fig:02} shows that end users will  gain to reduce the execution time
 for  both  algorithms  in using  a  grid  architecture  like  4x16 or  8x8:  the
-performance was increased  in a factor of  2. The results depict  also that when
+performance was increased  by a factor of  $2$. The results depict  also that when
 the  network speed  drops down  (12.5\%), the  difference between  the execution
 times can reach more than 25\%. \RC{c'est pas clair : la différence entre quoi et quoi?}
+\DL{pas clair}
 
 \subsubsection{Network latency impacts on performance}
 \ \\
@@ -571,7 +574,7 @@ times can reach more than 25\%. \RC{c'est pas clair : la différence entre quoi
  Network & N1 : bw=1Gbs \\ %\hline
  Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline
  \end{tabular}
-\caption{Network latency impact}
+\caption{Network latency impacts}
 \end{figure}
 
 
@@ -579,20 +582,20 @@ times can reach more than 25\%. \RC{c'est pas clair : la différence entre quoi
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf}
-\caption{Network latency impact on execution time}
+\caption{Network latency impacts on execution time}
 \label{fig:03}
 \end{figure}
 
 
-According  the results  in  Figure~\ref{fig:03}, a  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. Krylov
+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.   Consequently,  in  the  worst  case
-(lat=6.10$^{-5 }$), the  execution time for GMRES is almost  the double than the
+($lat=6.10^{-5 }$), 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$^{-6}$.
+order of magnitude with a latency of $8.10^{-6}$.
 
 \subsubsection{Network bandwidth impacts on performance}
 \ \\
@@ -604,24 +607,22 @@ order of magnitude with a latency of 8.10$^{-6}$.
  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}
-\caption{Network bandwidth impact}
+\caption{Network bandwidth impacts}
 \end{figure}
 
 
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf}
-\caption{Network bandwith impact on execution time}
+\caption{Network bandwith impacts on execution time}
 \label{fig:04}
 \end{figure}
 
-
-
 The results  of increasing  the network  bandwidth show  the improvement  of the
 performance  for   both  algorithms   by  reducing   the  execution   time  (see
 Figure~\ref{fig:04}). However,  in this  case, the Krylov  multisplitting method
 presents a better  performance in the considered bandwidth interval  with a gain
-of 40\% which is only around 24\% for classical GMRES.
+of $40\%$ which is only around $24\%$ for the classical GMRES.
 
 \subsubsection{Input matrix size impacts on performance}
 \ \\
@@ -630,30 +631,30 @@ of 40\% which is only around 24\% for classical GMRES.
 \begin{tabular}{r c }
  \hline
  Grid & 4x8\\ %\hline
- Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ 
+ Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\
  Input matrix size & N$_{x}$ = From 40 to 200\\ \hline
  \end{tabular}
-\caption{Input matrix size impact}
+\caption{Input matrix size impacts}
 \end{figure}
 
 
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf}
-\caption{Problem size impact on execution time}
+\caption{Problem size impacts on execution time}
 \label{fig:05}
 \end{figure}
 
-In these experiments, the input matrix size  has been set from 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 Figure~\ref{fig:05},  the execution
+In these experiments, the input matrix size  has been set from $N_{x} = N_{y}
+= N_{z} = 40$ to $200$ side elements  that is from $40^{3} = 64.000$ to $200^{3}
+= 8,000,000$  points. Obviously, as  shown in Figure~\ref{fig:05},  the execution
 time for  both algorithms increases when  the input matrix size  also increases.
 But the interesting results are:
 \begin{enumerate}
-  \item the drastic increase (300 times) \RC{Je ne vois pas cela sur la figure}
+  \item the drastic increase ($300$ times) \RC{Je ne vois pas cela sur la figure}
 of the  number of  iterations needed  to reach the  convergence for  the classical
-GMRES algorithm when  the matrix size go beyond N$_{x}$=150;
-\item the  classical GMRES execution time  is almost the double  for N$_{x}$=140
+GMRES algorithm when  the matrix size go beyond $N_{x}=150$;
+\item the  classical GMRES execution time  is almost the double  for $N_{x}=140$
   compared with the Krylov multisplitting method.
 \end{enumerate}
 
@@ -662,7 +663,7 @@ targeted environment for the application deployment when focusing on the problem
 size scale up.  It  should be noticed that the same test has  been done with the
 grid 2x16 leading to the same conclusion.
 
-\subsubsection{CPU Power impact on performance}
+\subsubsection{CPU Power impacts on performance}
 
 \begin{figure} [ht!]
 \centering
@@ -672,22 +673,26 @@ grid 2x16 leading to the same conclusion.
  Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
  Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline
  \end{tabular}
-\caption{CPU Power impact}
+\caption{CPU Power impacts}
 \end{figure}
 
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf}
-\caption{CPU Power impact on execution time}
+\caption{CPU Power impacts on execution time}
 \label{fig:06}
 \end{figure}
 
 Using the Simgrid  simulator flexibility, we have tried to  determine the impact
 on the  algorithms performance in  varying the CPU  power of the  clusters nodes
-from 1  to 19 GFlops.  The outputs  depicted in Figure~\ref{fig:06}  confirm the
-performance gain,  around 95\% for  both of the  two methods, after  adding more
+from $1$ to $19$ GFlops.  The outputs  depicted in Figure~\ref{fig:06}  confirm the
+performance gain,  around $95\%$ for  both of the  two methods, after  adding more
 powerful CPU.
 
+\DL{il faut une conclusion sur ces tests : ils confirment les résultats déjà
+obtenus en grandeur réelle. Donc c'est une aide précieuse pour les dev. Pas
+besoin de déployer sur une archi réelle}
+
 \subsection{Comparing GMRES in native synchronous mode and the multisplitting algorithm in asynchronous mode}
 
 The previous paragraphs  put in evidence the interests to  simulate the behavior