\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Sparse_omp}
-\caption{Execution time in seconds of the Ehrlich-Aberth method to solve sparse polynomials on multiple GPUs.}
+\caption{Execution time in seconds of the Ehrlich-Aberth method to
+ solve sparse polynomials on multiple GPUs with CUDA-OpenMP.}
\label{fig:01}
\end{figure}
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Full_omp}
-\caption{Execution time in seconds of the Ehrlich-Aberth method to solve full polynomials on multiple GPUs}
+\caption{Execution time in seconds of the Ehrlich-Aberth method to
+ solve full polynomials on multiple GPUs with CUDA-OpenMP.}
\label{fig:02}
\end{figure}
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Sparse_mpi}
-\caption{Execution time in seconds of the Ehrlich-Aberth method to solve sparse polynomials on multiple GPUs.}
+\caption{Execution time in seconds of the Ehrlich-Aberth method to
+ solve sparse polynomials on multiple GPUs with CUDA-MPI.}
\label{fig:03}
\end{figure}
Figure~\ref{fig:03} shows the execution times of te EA algorithm,
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Full_mpi}
-\caption{Execution times in seconds of the Ehrlich-Aberth method for full polynomials on GPUs using the Multi-GPU}
+\caption{Execution times in seconds of the Ehrlich-Aberth method for
+ full polynomials on multiple GPUs with CUDA-MPI.}
\label{fig:04}
\end{figure}
In Figure~\ref{fig:04}, we can also observe that the CUDA-MPI approach
is also efficient to solve full polynimails on multiple GPUs.
-\subsection{Comparing the CUDA-OpenMP approach and the CUDA-MPI approach}
-In the previuos section we saw that both approches are very effective in reducing execution time for sparse as well as full polynomials. At this stage, the interesting question is which approach is better. In the fellowing, we present appropriate experiments comparing the two Multi-GPU approaches to answer the question.
+\subsection{Comparison of the CUDA-OpenMP and the CUDA-MPI approaches}
+
+In the previuos section we saw that both approches are very effecient
+to reduce the execution times the sparse and full polynomials. In
+this section we try to compare these two approaches.
\subsubsection{Solving sparse polynomials}
In this experiment three sparse polynomials of size 200K, 800K and 1,4M are investigated.
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Sparse}
-\caption{Execution time for solving sparse polynomials of three distinct sizes on multiple GPUs using MPI and OpenMP approaches using Ehrlich-Aberth}
+\caption{Execution times to solvs sparse polynomials of three
+ distinct sizes on multiple GPUs using MPI and OpenMP with the
+ Ehrlich-Aberth method}
\label{fig:05}
\end{figure}
-In Figure~\ref{fig:05} there two curves for each polynomial size : one for the MPI-CUDA and another for the OpenMP. We can see that the results are similar between OpenMP and MPI for the polynomials size of 200K. For the size of 800K, the MPI version is a little slower than the OpenMP approach but for the 1,4 millions size, there is a slight advantage for the MPI version.
+In Figure~\ref{fig:05} there is one curve for CUDA-MPI and another one
+for CUDA-OpenMP. We can see that the results are quite similar between
+OpenMP and MPI for the polynomials size of 200K. For the size of 800K,
+the MPI version is a little bit slower than the OpenMP approach but for
+the 1,4 millions size, there is a slight advantage for the MPI
+version.
\subsubsection{Solving full polynomials}
\begin{figure}[htbp]
