algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially
in a grid computing context.
-\begin{figure}[htbp]
+\begin{figure}[!t]
\centering
\includegraphics[width=8cm]{AIAC.pdf}
\caption{The Asynchronous Iterations - Asynchronous Communications model }
\end{equation}
is solved independently by a cluster and communications are required to update the right-hand side sub-vector $Y_l$, such that the sub-vectors $X_m$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers.
-\begin{figure}
+\begin{figure}[!t]
%%% IEEE instructions forbid to use an algorithm environment here, use figure
%%% instead
\begin{algorithmic}[1]
elements of the solution $x$ are exchanged by message passing using MPI
non-blocking communication routines.
-\begin{figure}
+\begin{figure}[!t]
\centering
\includegraphics[width=60mm,keepaspectratio]{clustering}
\caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.}
62 \text{ to } 171$ elements or from $62^{3} = \np{238328}$ to $171^{3} =
\np{5211000}$ entries.
-\begin{table}
+\begin{table}[!t]
\centering
\caption{2 clusters, each with 50 nodes}
\label{tab.cluster.2x50}
permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the
speedups less than 1 with a matrix size from 62 to 100 elements.
-\begin{table}
+\begin{table}[!t]
\centering
\caption{3 clusters, each with 33 nodes}
\label{tab.cluster.3x33}
configuration but increasing by two hundreds hosts has been recorded in
Table~\ref{tab.cluster.3x67}.
-\begin{table}
+\begin{table}[!t]
\centering
\caption{3 clusters, each with 66 nodes}
\label{tab.cluster.3x67}
