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
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...}
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!]
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}
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}
\ \\
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}
\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}
\ \\
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
\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}
