+@article{cz15:ij,
+inhal = {no},
+domainehal = {INFO:INFO_DC, INFO:INFO_CR, INFO:INFO_MO},
+equipe = {and},
+classement = {ACLI},
+impact-factor ={0.841},
+isi-acro = {J SUPERCOMPUT},
+author = {Couturier, Rapha\"el and Ziane Khodja, Lilia},
+title = {A scalable multisplitting algorithm to solve large sparse linear systems},
+journal = {The journal of Supercomputing},
+note={{O}nline version, 10.1007/s11227-014-1367-7},
+publisher = {Springer},
+year = 2015,
+
+}
\ No newline at end of file
s}$, with $s\ll n$. In order to minimize~\eqref{eq:01}, a least-squares
method such as CGLS ~\cite{Hestenes52} or LSQR~\cite{Paige82} is used. Remark
that these methods are more appropriate than a single direct method in a
-parallel context.
+parallel context. CGLS has recently been used to improve the performance of multisplitting algorithms \cite{cz15:ij}.
\begin{figure}[htbp]
\centering
- \includegraphics[width=0.45\textwidth]{nb_iter_sec_ex15_juqueen}
+ \includegraphics[width=0.5\textwidth]{nb_iter_sec_ex15_juqueen}
\caption{Number of iterations per second with ex15 and the same parameters as in Table~\ref{tab:03} (weak scaling)}
\label{fig:01}
\end{figure}
\begin{figure}[htbp]
\centering
- \includegraphics[width=0.45\textwidth]{nb_iter_sec_ex54_curie}
+ \includegraphics[width=0.5\textwidth]{nb_iter_sec_ex54_curie}
\caption{Number of iterations per second with ex54 and the same parameters as in Table~\ref{tab:05} (strong scaling)}
\label{fig:02}
\end{figure}
%%%*********************************************************
\section*{Acknowledgment}
This paper is partially funded by the Labex ACTION program (contract
-ANR-11-LABX-01-01). We acknowledge PRACE for awarding us access to resources
+ANR-11-LABX-01-01). We acknowledge the Mesocentre of Franche-Comte and PRACE for awarding us access to resources
Curie and Juqueen respectively based in France and Germany.