X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper2.git/blobdiff_plain/b34893a990fb0c4094c477543a475e91d15cde25..18956ad6588c4d4035809466f3da0088916a3a74:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index 0b2d113..d6dd2ff 100644
--- a/paper.tex
+++ b/paper.tex
@@ -351,22 +351,16 @@
 % author names and affiliations
 % use a multiple column layout for up to three different
 % affiliations
-\author{\IEEEauthorblockN{Michael Shell}
-\IEEEauthorblockA{School of Electrical and\\Computer Engineering\\
-Georgia Institute of Technology\\
-Atlanta, Georgia 30332--0250\\
-Email: http://www.michaelshell.org/contact.html}
+\author{\IEEEauthorblockN{Kahina Guidouche, Abderrahmane Sider }
+  \IEEEauthorblockA{Laboratoire LIMED\\
+    FacultÃ© des sciences exactes\\
+    UniversitÃ© de Bejaia, 06000, Algeria\\
+Email: \{kahina.ghidouche,ar.sider\}@univ-bejaia.dz}
 \and
-\IEEEauthorblockN{Homer Simpson}
-\IEEEauthorblockA{Twentieth Century Fox\\
-Springfield, USA\\
-Email: homer@thesimpsons.com}
-\and
-\IEEEauthorblockN{James Kirk\\ and Montgomery Scott}
-\IEEEauthorblockA{Starfleet Academy\\
-San Francisco, California 96678--2391\\
-Telephone: (800) 555--1212\\
-Fax: (888) 555--1212}}
+\IEEEauthorblockN{Lilia Ziane Khodja, RaphaÃ«l Couturier}
+\IEEEauthorblockA{FEMTO-ST Institute\\
+  University of   Bourgogne Franche-Comte, France\\
+Email: zianekhodja.lilia@gmail.com\\ raphael.couturier@univ-fcomte.fr}}
 
 % conference papers do not typically use \thanks and this command
 % is locked out in conference mode. If really needed, such as for
@@ -795,13 +789,10 @@ implementation of Ehrlich-Aberth method.
 \item Initialization of Pu\;
 \item Initialization of the solution vector $Z^{0}$\;
 \item Allocate and copy initial data to the GPU global memory\;
-\item k=0\;
 \item \While {$\Delta z_{max} > \epsilon$}{
-\item   Let $\Delta z_{max}=0$\;
 \item   $ kernel\_save(ZPrec,Z)$\;
-\item   k=k+1\;
 \item   $ kernel\_update(Z,P,Pu)$\;
-\item $kernel\_testConverge(\Delta z_{max},Z,ZPrec)$\;
+\item $\Delta z_{max}=kernel\_testConverge(Z,ZPrec)$\;
 
 }
 \item Copy results from GPU memory to CPU memory\;
@@ -1078,14 +1069,22 @@ version.
 In Figure~\ref{fig:06}, we can see that when it comes to full polynomials, both approaches are almost equivalent.
 
 \subsubsection{Solving sparse and full polynomials of the same size with CUDA-MPI}
-In this experiment we compare the execution time of the EA algorithm according to the number of GPUs for solving sparse and full polynomials on Multi-GPU using MPI. We chose three sparse and full polynomials of size 200K, 800K  and 1,4M. 
+
+In this experiment we compare the execution time of the EA algorithm
+according to the number of GPUs to solve sparse and full
+polynomials on multiples GPUs using MPI. We chose three sparse and full
+polynomials of size 200K, 800K and 1,4M.
 \begin{figure}[htbp]
 \centering
  \includegraphics[angle=-90,width=0.5\textwidth]{MPI}
-\caption{Execution time for solving sparse and full polynomials of three distinct sizes on multiple GPUs using MPI}
+\caption{Execution times to solve sparse and full polynomials of three distinct sizes on multiple GPUs using MPI.}
 \label{fig:07}
 \end{figure}
-in figure ~\ref{fig:07} we can see that CUDA-MPI can solve sparse and full polynomials of high degrees, the execution time with sparse polynomial are very low comparing to full polynomials. with sparse polynomials the number of monomial are reduce, consequently the number of operation are reduce than the execution time decrease. 
+In Figure~\ref{fig:07} we can see that CUDA-MPI can solve sparse and
+full polynomials of high degrees, the execution times with sparse
+polynomial are very low compared to full polynomials. With sparse
+polynomials the number of monomials is reduced, consequently the number
+of operations is reduced and the execution time decreases.
 
 \subsubsection{Solving sparse and full polynomials of the same size with CUDA-OpenMP}
 
@@ -1096,17 +1095,23 @@ in figure ~\ref{fig:07} we can see that CUDA-MPI can solve sparse and full polyn
 \label{fig:08}
 \end{figure}
 
-Figure ~\ref{fig:08} shows the impact of sparsity on the effectiveness of the CUDA-OpenMP approach. We can see that the impact fellows the same pattern, a difference in execution time in favor of the sparse polynomials. 
-%SIDER : il faut une explication ici. je ne vois pas de prime abords, qu'est-ce qui engendre cette diffÃ©rence, car quelques soient les coefficients nulls ou non nulls, c'est toutes les racines qui sont calculÃ©es qu'elles soient similaires ou non (degrÃ©s de multiplicitÃ©).
-\subsection{Scalability of the EA method on Multi-GPU to solve very high degree polynomials}
-These experiments report the execution time according to the degrees of polynomials ranging from 1,000,000 to 5,000,000 for both approaches with sparse and full  polynomials. 
+Figure ~\ref{fig:08} shows the impact of sparsity on the effectiveness of the CUDA-OpenMP approach. We can see that the impact follows the same pattern, a difference in execution time in favor of the sparse polynomials. 
+
+\subsection{Scalability of the EA method on multiple GPUs to solve very high degree polynomials}
+These experiments report the execution times of the EA method for
+sparse and full polynomials ranging from 1,000,000 to 5,000,000.
 \begin{figure}[htbp]
 \centering
  \includegraphics[angle=-90,width=0.5\textwidth]{big}
  \caption{Execution times in seconds of the Ehrlich-Aberth method for solving full polynomials of high degree on 4 GPUs for sizes ranging from 1M to 5M}
 \label{fig:09}
 \end{figure}
-In figure ~\ref{fig:09} we can see that both approaches are scalable and can solve very high degree polynomials. With full polynomial both approaches give interestingly very similar results. For the sparse case however, there are a noticeable difference in favour of MPI when the degree is above 4M. Between 1M and 3M, the OMP approach is more effective and under 1M degree, OMP and MPI approaches are almost equivalent.
+In Figure~\ref{fig:09} we can see that both approaches are scalable
+and can solve very high degree polynomials. With full polynomial both
+approaches give very similar results. However, for sparse polynomials
+there are a noticeable difference in favour of MPI when the degree is
+above 4 millions. Between 1  and 3 millions, OpenMP is more effecient.
+Under 1 million, OpenMPI and MPI are almost equivalent.
 
 %SIDER : il faut une explication sur les diffÃ©rences ici aussi.
  
@@ -1210,13 +1215,25 @@ In figure ~\ref{fig:09} we can see that both approaches are scalable and can sol
 
 \section{Conclusion}
 \label{sec6}
-In this paper, we have presented a parallel implementation of Ehrlich-Aberth algorithm for solving full and sparse polynomials, on single GPU with CUDA and on multiple GPUs using two parallel paradigms : shared memory with OpenMP and distributed memory with MPI. These architectures were addressed by a CUDA-OpenMP approach and CUDA-MPI approach, respectively. 
-The experiments show that, using parallel programming model like (OpenMP, MPI), we can efficiently manage multiple graphics cards to work together to solve the same problem and accelerate the parallel execution with 4 GPUs and solve a polynomial of degree 1,000,000, four times faster than on single GPU, that is a quasi-linear speedup.
+In this paper, we have presented a parallel implementation of
+Ehrlich-Aberth algorithm to solve full and sparse polynomials, on
+single GPU with CUDA and on multiple GPUs using two parallel
+paradigms: shared memory with OpenMP and distributed memory with
+MPI. These architectures were addressed by a CUDA-OpenMP approach and
+CUDA-MPI approach, respectively.  Experiments show that, using
+parallel programming model like (OpenMP, MPI). We can efficiently
+manage multiple graphics cards to solve the same
+problem and accelerate the parallel execution with 4 GPUs and solve a
+polynomial of degree up to 5,000,000, four times faster than on single
+GPU. 
 
 
 %In future, we will evaluate our parallel implementation of Ehrlich-Aberth algorithm on other parallel programming model 
 
-Our next objective is to extend the model presented here at clusters of nodes featuring  multiple GPUs, with a three-level scheme: inter-node communication via MPI processes (distributed memory), management of multi-GPU node by OpenMP threads (shared memory).
+Our next objective is to extend the model presented here with clusters
+of GPU nodes, with a three-level scheme: inter-node communication via
+MPI processes (distributed memory), management of multi-GPU node by
+OpenMP threads (shared memory).
 
 %present a communication approach between multiple GPUs. The comparison between MPI and OpenMP as GPUs controllers shows that these
 %solutions can effectively manage multiple graphics cards to work together
@@ -1234,8 +1251,10 @@ Our next objective is to extend the model presented here at clusters of nodes fe
 % use section* for acknowledgment
 \section*{Acknowledgment}
 
+Computations have been performed on the supercomputer facilities of
+the MÃ©socentre de calcul de Franche-ComtÃ©. We also would like to thank
+Nvidia for hardware donation under CUDA Research Center 2014.
 
-The authors would like to thank...
 
 
 
