commenter la premiere figure

[kahina_paper2.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index 34d2fb498361becb357ac0e2ce5f2515c3fb13f3..4a9d058f2651562509ecedccf60b4c2cc0bc9e32 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -784,19 +784,24 @@ to $10^{-7}$.
 %CPUs versus on GPUs.
 The initialization values of the vector solution
 of the methods are given in %Section~\ref{sec:vec_initialization}.
 %CPUs versus on GPUs.
 The initialization values of the vector solution
 of the methods are given in %Section~\ref{sec:vec_initialization}.

+
+\subsection{Test with (CUDA OpenMP) approach}
+
+In this part we performed  a set of experiments with (CUDA OpenMP) approach on full and sparse polynomials of different degrees.
+ \subsubsection{Execution times in seconds of the Ehrlich-Aberth method for solving sparse polynomials on GPUs using shared memory paradigm with OpenMP}
+ 
+ In this test we report the execution time of the EA algorithm, on single GPU and Multi-GPU with (2,3,4) GPUs, for different sparse polynomial degrees ranging from 100,000 to 1,400,000

 \begin{figure}[htbp]
 \begin{figure}[htbp]

+

 \centering
   \includegraphics[angle=-90,width=0.5\textwidth]{Sparse_omp}
 \caption{Execution times in seconds of the Ehrlich-Aberth method for solving sparse polynomials on GPUs using shared memory paradigm with OpenMP}
 \label{fig:01}
 \end{figure}
 \centering
   \includegraphics[angle=-90,width=0.5\textwidth]{Sparse_omp}
 \caption{Execution times in seconds of the Ehrlich-Aberth method for solving sparse polynomials on GPUs using shared memory paradigm with OpenMP}
 \label{fig:01}
 \end{figure}

+in this figure~\ref{fig:01} shows that (CUDA OpenMP) Multi-GPU approach reduce the execution time up to the scale 100 whereas single GPU is of scale 1000 for polynomial who exceed 1,000,000. It shows the advantage to use OpenMP parallel paradigm  to connect the performances of several GPUs and solve a high polynomial of degrees.   
+
+\subsubsection{Execution times in seconds of the Ehrlich-Aberth method for solving full polynomials on GPUs using shared memory paradigm with OpenMP}

 
 

-\begin{figure}[htbp]
-\centering
-  \includegraphics[angle=-90,width=0.5\textwidth]{Sparse_mpi}
-\caption{Execution times in seconds of the Ehrlich-Aberth method for solving sparse polynomials on GPUs using distributed memory paradigm with MPI}
-\label{fig:02}
-\end{figure}

 
 \begin{figure}[htbp]
 \centering
 
 \begin{figure}[htbp]
 \centering


@@ -805,6 +810,15 @@ of the methods are given in %Section~\ref{sec:vec_initialization}.
 \label{fig:03}
 \end{figure}
 
 \label{fig:03}
 \end{figure}
 

+
+\begin{figure}[htbp]
+\centering
+  \includegraphics[angle=-90,width=0.5\textwidth]{Sparse_mpi}
+\caption{Execution times in seconds of the Ehrlich-Aberth method for solving sparse polynomials on GPUs using distributed memory paradigm with MPI}
+\label{fig:02}
+\end{figure}
+
+

 \begin{figure}[htbp]
 \centering
   \includegraphics[angle=-90,width=0.5\textwidth]{Full_mpi}
 \begin{figure}[htbp]
 \centering
   \includegraphics[angle=-90,width=0.5\textwidth]{Full_mpi}