\todo[color=red!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
\newcommand{\RC}[2][inline]{%
\todo[color=blue!10,#1]{\sffamily\textbf{RC:} #2}\xspace}
+\newcommand{\KG}[2][inline]{%
+ \todo[color=green!10,#1]{\sffamily\textbf{KG:} #2}\xspace}
+\newcommand{\AS}[2][inline]{%
+ \todo[color=orange!10,#1]{\sffamily\textbf{AS:} #2}\xspace}
% 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\\
+ Faculté des sciences exactes\\
+ Université 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, Raphaël 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
\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\;
\end{enumerate}
~\\
-
+\RC{Au final, on laisse ce code, on l'explique, si c'est kahina qui
+ rajoute l'explication, il faut absolument ajouter \KG{dfsdfsd}, car
+ l'anglais sera à relire et je ne veux pas tout relire... }
\section{The EA algorithm on Multiple GPUs}
\label{sec4}
\subsection{M-GPU : an OpenMP-CUDA approach}
-Our OpenMP-CUDA implementation of EA algorithm is based on the hybrid OpenMP and CUDA programming model. It works as follows.
-Based on the metadata, a shared memory is used to make data evenly shared among OpenMP threads. The shared data are the solution vector $Z$, the polynomial to solve $P$, and the error vector $\Delta z$. Let (T\_omp) the number of OpenMP threads be equal to the number of GPUs, each OpenMP thread binds to one GPU, and controls a part of the shared memory, that is a part of the vector Z , that is $(n/num\_gpu)$ roots where $n$ is the polynomial's degree and $num\_gpu$ the total number of available GPUs. Each OpenMP thread copies its data from host memory to GPU’s device memory. Then every GPU will have a grid of computation organized according to the device performance and the size of data on which it runs the computation kernels. %In principle a grid is set by two parameter DimGrid, the number of block per grid, DimBloc: the number of threads per block. The following schema shows the architecture of (CUDA,OpenMP).
+Our OpenMP-CUDA implementation of EA algorithm is based on the hybrid
+OpenMP and CUDA programming model. All the data
+are shared with OpenMP amoung all the OpenMP threads. The shared data
+are the solution vector $Z$, the polynomial to solve $P$, and the
+error vector $\Delta z$. The number of OpenMP threads is equal to the
+number of GPUs, each OpenMP thread binds to one GPU, and it controls a
+part of the shared memory. More precisely each OpenMP thread owns of
+the vector Z, that is $(n/num\_gpu)$ roots where $n$ is the
+polynomial's degree and $num\_gpu$ the total number of available
+GPUs. Then all GPUs will have a grid of computation organized
+according to the device performance and the size of data on which it
+runs the computation kernels.
+
+To compute one iteration of the EA method each GPU performs the
+followings steps. First roots are shared with OpenMP. Each thread
+starts by copying all the previous roots inside its GPU. Then each GPU
+will compute an iteration of the EA method on its own roots. For that
+all the other roots are used. At the end of an iteration, the updated
+roots are copied from the GPU to the CPU. The convergence is checked
+on the new roots. Finally each CPU will update its own roots in the
+shared memory arrays containing all the roots.
+
+%In principle a grid is set by two parameter DimGrid, the number of block per grid, DimBloc: the number of threads per block. The following schema shows the architecture of (CUDA,OpenMP).
%\begin{figure}[htbp]
%\centering
%\end{figure}
%Each thread OpenMP compute the kernels on GPUs,than after each iteration they copy out the data from GPU memory to CPU shared memory. The kernels are re-runs is up to the roots converge sufficiently. Here are below the corresponding algorithm:
-$num\_gpus$ OpenMP threads are created using \verb=omp_set_num_threads();=function (step $3$, Algorithm \ref{alg2-cuda-openmp}), the shared memory is created using \verb=#pragma omp parallel shared()= OpenMP function (line $5$, Algorithm\ref{alg2-cuda-openmp}), then each OpenMP thread allocates memory and copies initial data from CPU memory to GPU global memory, executes the kernels on GPU, but computes only his portion of roots indicated with variable \textit{index} initialized in (line 5, Algorithm \ref{alg2-cuda-openmp}), used as input data in the $kernel\_update$ (line 10, Algorithm \ref{alg2-cuda-openmp}). After each iteration, all OpenMP threads synchronize using \verb=#pragma omp barrier;= to gather all the correct values of $\Delta z$, thus allowing the computation the maximum stop condition on vector $\Delta z$ (line 12, Algorithm \ref{alg2-cuda-openmp}). Finally, threads copy the results from GPU memories to CPU memory. The OpenMP threads execute kernels until the roots sufficiently converge.
-\begin{enumerate}
-\begin{algorithm}[htpb]
-\label{alg2-cuda-openmp}
-%\LinesNumbered
-\caption{CUDA-OpenMP Algorithm to find roots with the Ehrlich-Aberth method}
-
-\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
- threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degree), $\Delta z$ ( Vector of errors for stop condition), $num_gpus$ (number of OpenMP threads/ Number of GPUs), $Size$ (number of roots)}
-
-\KwOut {$Z$ ( Root's vector), $ZPrec$ (Previous root's vector)}
-
-\BlankLine
-
-\item Initialization of P\;
-\item Initialization of Pu\;
-\item Initialization of the solution vector $Z^{0}$\;
-\verb=omp_set_num_threads(num_gpus);=
-\verb=#pragma omp parallel shared(Z,$\Delta$ z,P);=
-\verb=cudaGetDevice(gpu_id);=
-\item Allocate and copy initial data from CPU memory to the GPU global memories\;
-\item index= $Size/num\_gpus$\;
-\item k=0\;
-\While {$error > \epsilon$}{
-\item Let $\Delta z=0$\;
-\item $ kernel\_save(ZPrec,Z)$\;
-\item k=k+1\;
-\item $ kernel\_update(Z,P,Pu,index)$\;
-\item $kernel\_testConverge(\Delta z[gpu\_id],Z,ZPrec)$\;
-%\verb=#pragma omp barrier;=
-\item error= Max($\Delta z$)\;
-}
-
-\item Copy results from GPU memories to CPU memory\;
-\end{algorithm}
-\end{enumerate}
-~\\
-
+%% \RC{Surement à virer ou réécrire pour etre compris sans algo}
+%% $num\_gpus$ OpenMP threads are created using
+%% \verb=omp_set_num_threads();=function (step $3$, Algorithm
+%% \ref{alg2-cuda-openmp}), the shared memory is created using
+%% \verb=#pragma omp parallel shared()= OpenMP function (line $5$,
+%% Algorithm\ref{alg2-cuda-openmp}), then each OpenMP thread allocates
+%% memory and copies initial data from CPU memory to GPU global memory,
+%% executes the kernels on GPU, but computes only his portion of roots
+%% indicated with variable \textit{index} initialized in (line 5,
+%% Algorithm \ref{alg2-cuda-openmp}), used as input data in the
+%% $kernel\_update$ (line 10, Algorithm \ref{alg2-cuda-openmp}). After
+%% each iteration, all OpenMP threads synchronize using
+%% \verb=#pragma omp barrier;= to gather all the correct values of
+%% $\Delta z$, thus allowing the computation the maximum stop condition
+%% on vector $\Delta z$ (line 12, Algorithm
+%% \ref{alg2-cuda-openmp}). Finally, threads copy the results from GPU
+%% memories to CPU memory. The OpenMP threads execute kernels until the
+%% roots sufficiently converge.
+
+
+%% \begin{enumerate}
+%% \begin{algorithm}[htpb]
+%% \label{alg2-cuda-openmp}
+%% %\LinesNumbered
+%% \caption{CUDA-OpenMP Algorithm to find roots with the Ehrlich-Aberth method}
+
+%% \KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
+%% threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degree), $\Delta z$ ( Vector of errors for stop condition), $num_gpus$ (number of OpenMP threads/ Number of GPUs), $Size$ (number of roots)}
+
+%% \KwOut {$Z$ ( Root's vector), $ZPrec$ (Previous root's vector)}
+
+%% \BlankLine
+
+%% \item Initialization of P\;
+%% \item Initialization of Pu\;
+%% \item Initialization of the solution vector $Z^{0}$\;
+%% \verb=omp_set_num_threads(num_gpus);=
+%% \verb=#pragma omp parallel shared(Z,$\Delta$ z,P);=
+%% \verb=cudaGetDevice(gpu_id);=
+%% \item Allocate and copy initial data from CPU memory to the GPU global memories\;
+%% \item index= $Size/num\_gpus$\;
+%% \item k=0\;
+%% \While {$error > \epsilon$}{
+%% \item Let $\Delta z=0$\;
+%% \item $ kernel\_save(ZPrec,Z)$\;
+%% \item k=k+1\;
+%% \item $ kernel\_update(Z,P,Pu,index)$\;
+%% \item $kernel\_testConverge(\Delta z[gpu\_id],Z,ZPrec)$\;
+%% %\verb=#pragma omp barrier;=
+%% \item error= Max($\Delta z$)\;
+%% }
+
+%% \item Copy results from GPU memories to CPU memory\;
+%% \end{algorithm}
+%% \end{enumerate}
+%% ~\\
+%% \RC{C'est encore pire ici, on ne voit pas les comm CPU <-> GPU }
\subsection{Multi-GPU : an MPI-CUDA approach}
Since a GPU works only on data already allocated in its memory, all local input data, $Z_{k}$, $ZPrec$ and $\Delta z_{k}$, must be transferred from CPU memories to the corresponding GPU memories. Afterwards, the same EA algorithm (Algorithm \ref{alg1-cuda}) is run by all processes but on different polynomial subset of roots $ p(x)_{k}=\sum_{i=1}^{n} a_{i}x^{i}, k=1,...,p$. Each MPI process executes the loop \verb=(While(...)...do)= containing the CUDA kernels but each MPI process computes only its own portion of the roots according to the rule ``''owner computes``''. The local range of roots is indicated with the \textit{index} variable initialized at (line 5, Algorithm \ref{alg2-cuda-mpi}), and passed as an input variable to $kernel\_update$ (line 10, Algorithm \ref{alg2-cuda-mpi}). After each iteration, MPI processes synchronize (\verb=MPI_Allreduce= function) by a reduction on $\Delta z_{k}$ in order to compute the maximum error related to the stop condition. Finally, processes copy the values of new computed roots from GPU memories to CPU memories, then communicate their results to other processes with \verb=MPI_Alltoall= broadcast. If the stop condition is not verified ($error > \epsilon$) then processes stay withing the loop \verb= while(...)...do= until all the roots sufficiently converge.
-\begin{enumerate}
-\begin{algorithm}[htpb]
-\label{alg2-cuda-mpi}
-%\LinesNumbered
-\caption{CUDA-MPI Algorithm to find roots with the Ehrlich-Aberth method}
-
-\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
- threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degrees), $\Delta z$ ( error of stop condition), $num_gpus$ (number of MPI processes/ number of GPUs), Size (number of roots)}
-
-\KwOut {$Z$ (Solution root's vector), $ZPrec$ (Previous solution root's vector)}
-
-\BlankLine
-\item Initialization of P\;
-\item Initialization of Pu\;
-\item Initialization of the solution vector $Z^{0}$\;
-\item Allocate and copy initial data from CPU memories to GPU global memories\;
-\item $index= Size/num_gpus$\;
-\item k=0\;
-\While {$error > \epsilon$}{
-\item Let $\Delta z=0$\;
-\item $kernel\_save(ZPrec,Z)$\;
-\item k=k+1\;
-\item $kernel\_update(Z,P,Pu,index)$\;
-\item $kernel\_testConverge(\Delta z,Z,ZPrec)$\;
-\item ComputeMaxError($\Delta z$,error)\;
-\item Copy results from GPU memories to CPU memories\;
-\item Send $Z[id]$ to all processes\;
-\item Receive $Z[j]$ from every other process j\;
-}
-\end{algorithm}
-\end{enumerate}
-~\\
+%% \begin{enumerate}
+%% \begin{algorithm}[htpb]
+%% \label{alg2-cuda-mpi}
+%% %\LinesNumbered
+%% \caption{CUDA-MPI Algorithm to find roots with the Ehrlich-Aberth method}
+
+%% \KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
+%% threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degrees), $\Delta z$ ( error of stop condition), $num_gpus$ (number of MPI processes/ number of GPUs), Size (number of roots)}
+
+%% \KwOut {$Z$ (Solution root's vector), $ZPrec$ (Previous solution root's vector)}
+
+%% \BlankLine
+%% \item Initialization of P\;
+%% \item Initialization of Pu\;
+%% \item Initialization of the solution vector $Z^{0}$\;
+%% \item Allocate and copy initial data from CPU memories to GPU global memories\;
+%% \item $index= Size/num_gpus$\;
+%% \item k=0\;
+%% \While {$error > \epsilon$}{
+%% \item Let $\Delta z=0$\;
+%% \item $kernel\_save(ZPrec,Z)$\;
+%% \item k=k+1\;
+%% \item $kernel\_update(Z,P,Pu,index)$\;
+%% \item $kernel\_testConverge(\Delta z,Z,ZPrec)$\;
+%% \item ComputeMaxError($\Delta z$,error)\;
+%% \item Copy results from GPU memories to CPU memories\;
+%% \item Send $Z[id]$ to all processes\;
+%% \item Receive $Z[j]$ from every other process j\;
+%% }
+%% \end{algorithm}
+%% \end{enumerate}
+%% ~\\
+
+%% \RC{ENCORE ENCORE PIRE}
\section{Experiments}
\label{sec5}
\begin{equation}
{\Large \forall a_{i} \in C, i\in N; p(x)=\sum^{n}_{i=0} a_{i}.x^{i}}
\end{equation}
-For our tests, a CPU Intel(R) Xeon(R) CPU E5620@2.40GHz and a GPU K40 (with 6 Go of ram) are used.
-%SIDER : Une meilleure présentation de l'architecture est à faire ici.
-For our test, a cluster of computing with 72 nodes, 1116 cores, 4 cards GPU tesla Kepler K40 are used,
-In order to evaluate both the M-GPU and Multi-GPU approaches, we performed a set of experiments on a single GPU and multiple GPUs using OpenMP or MPI by EA algorithm, for both sparse and full polynomials of different sizes.
-All experimental results obtained are made in double precision data whereas the convergence threshold of the EA method is set to $10^{-7}$.
-%Since we were more interested in the comparison of the
-%performance behaviors of Ehrlich-Aberth and Durand-Kerner methods on
-%CPUs versus on GPUs.
-The initialization values of the vector solution
-of the methods are given by Guggenheimer method~\cite{Gugg86} %Section~\ref{sec:vec_initialization}.
-
-\subsection{Evaluating the M-GPU (CUDA-OpenMP) approach}
-
-We report here the results of the set of experiments with the M-GPU approach for full and sparse polynomials of different degrees, and we compare it with a Single GPU execution.
-\subsubsection{Execution time of the EA method for solving sparse polynomials on multiple GPUs using the M-GPU approach}
+
+For our test, 4 cards GPU tesla Kepler K40 are used. In order to
+evaluate both the GPU and Multi-GPU approaches, we performed a set of
+experiments on a single GPU and multiple GPUs using OpenMP or MPI with
+the EA algorithm, for both sparse and full polynomials of different
+sizes. All experimental results obtained are perfomed with double
+precision float data and the convergence threshold of the EA method is
+set to $10^{-7}$. The initialization values of the vector solution of
+the methods are given by Guggenheimer method~\cite{Gugg86}.
+
+
+\subsection{Evaluation of the CUDA-OpenMP approach}
+
+Here we report some experiments witt full and sparse polynomials of
+different degrees with multiple GPUs.
+\subsubsection{Execution times of the EA method to solve sparse polynomials on multiple GPUs}
-In this experiments 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.
+In this experiments we report the execution time of the EA algorithm, on single GPU and multi-GPUs with (2,3,4) GPUs, for different sparse polynomial degrees ranging from 100,000 to 1,400,000.
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Sparse_omp}
-\caption{Execution time in seconds of the Ehrlich-Aberth method for solving sparse polynomials on multiple GPUs using the M-GPU approach}
+\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}
-This figure~\ref{fig:01} shows that the (CUDA-OpenMP) M-GPU approach reduces the execution time by a factor up to 100 w.r.t the single GPU approach and a by a factor of 1000 for polynomials exceeding degree 1,000,000. It shows the advantage to use the OpenMP parallel paradigm to gather the capabilities of several GPUs and solve polynomials of very high degrees.
+Figure~\ref{fig:01} shows that the CUDA-OpenMP approach scales well
+with multiple GPUs. This version allows us to solve sparse polynomials
+of very high degrees.
-\subsubsection{Execution time in seconds of the Ehrlich-Aberth method for solving full polynomials on multiple GPUs using the M-GPU approach}
+\subsubsection{Execution times of the EA method to solve full polynomials on multiple GPUs}
-The experiments shows the execution time of the EA algorithm, on a single GPU and on multiple GPUs using the CUDA OpenMP approach for full polynomials of degrees ranging from 100,000 to 1,400,000.
+These experiments show the execution times of the EA algorithm, on a single GPU and on multiple GPUs using the CUDA OpenMP approach for full polynomials of degrees ranging from 100,000 to 1,400,000.
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Full_omp}
-\caption{Execution time in seconds of the Ehrlich-Aberth method for solving full polynomials on multiple GPUs using the M-GPU appraoch}
-\label{fig:03}
+\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}
-Results with full polynomials show very important savings in execution time. For a polynomial of degree 1,4 million, the CUDA-OpenMP approach with 4 GPUs solves it 4 times as fast as single GPU, thus achieving a quasi-linear speedup.
+In Figure~\ref{fig:02}, we can observe that with full polynomials the EA version with
+CUDA-OpenMP scales also well. Using 4 GPUs allows us to achieve a
+quasi-linear speedup.
-\subsection{Evaluating the Multi-GPU (CUDA-MPI) approach}
-In this part we perform a set of experiments to compare the Multi-GPU (CUDA MPI) approach with a single GPU, for solving full and sparse polynomials of degrees ranging from 100,000 to 1,400,000.
+\subsection{Evaluation of the CUDA-MPI approach}
+In this part we perform some experiments to evaluate the CUDA-MPI
+approach to solve full and sparse polynomials of degrees ranging from
+100,000 to 1,400,000.
-\subsubsection{Execution time of the Ehrlich-Aberth method for solving sparse polynomials on multiple GPUs using the Multi-GPU approach}
+\subsubsection{Execution times of the EA method to solve sparse polynomials on multiple GPUs}
\begin{figure}[htbp]
\centering
\includegraphics[angle=-90,width=0.5\textwidth]{Sparse_mpi}
-\caption{Execution time in seconds of the Ehrlich-Aberth method for solving sparse polynomials on multiple GPUs using the Multi-GPU approach}
-\label{fig:02}
+\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:02} shows execution time of EA algorithm, for a single GPU, and multiple GPUs (2, 3, 4) on respectively 2, 3 and four MPI nodes. We can clearly see that the curve for a single GPU is above the other curves, which shows overtime in execution time compared to the Multi-GPU approach. We can see also that the CUDA-MPI approach reduces the execution time by a factor of 10 for polynomials of degree more than 1,000,000. For example, at degree 1,000,000, the execution time with a single GPU amounted to 10 thousand seconds, while with 4 GPUs, it is lowered to about just one thousand seconds which makes it for a tenfold speedup.
-%%SIDER : Je n'ai pas reformuler car je n'ai pas compris la phrase, merci de l'ecrire ici en fran\cais.
-\\cette figure montre 4 courbes de temps d'exécution pour l'algorithme EA, une courbe avec un seul GPU, 3 courbes pour multiple GPUs(2, 3, 4), on peut constaté clairement que la courbe à un seul GPU est au-dessus des autres courbes, vue sa consommation en temps d'exècution. On peut voir aussi qu'avec l'approche Multi-GPU (CUDA-MPI) reduit le temps d'exècution jusqu'à l'echelle 100 pour le polynômes qui dépasse 1,000,000 tandis que Single GPU est de l'echelle 1000.
+Figure~\ref{fig:03} shows the execution times of te EA algorithm,
+for a single GPU, and multiple GPUs (2, 3, 4) with the CUDA-MPI approach.
\subsubsection{Execution time of the Ehrlich-Aberth method for solving full polynomials on multiple GPUs using the Multi-GPU appraoch}
\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.
- Figure \ref{fig:04} shows execution time for a single GPU, and multiple GPUs (2, 3, 4) on respectively 2, 3 and four MPI nodes. With the CUDA-MPI approach, we notice that the three curves are distinct from each other, more we use GPUs more the execution time decreases. On the other hand the curve for a single GPU is well above the other curves.
+\subsection{Comparison of the CUDA-OpenMP and the CUDA-MPI approaches}
-This is due to the use of MPI parallel paradigm that divides the problem computations and assigns portions to each GPU. But unlike the single GPU which carries all the computations on a single GPU, data communications are introduced, consequently engendering more execution time. But experiments show that execution time is still highly reduced.
-
-
-
-\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.
+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]
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}
\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 différence, car quelques soient les coefficients nulls ou non nulls, c'est toutes les racines qui sont calculées qu'elles soient similaires ou non (degrés de multiplicité).
-\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 différences ici aussi.
\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
% use section* for acknowledgment
\section*{Acknowledgment}
+Computations have been performed on the supercomputer facilities of
+the Mésocentre de calcul de Franche-Comté. We also would like to thank
+Nvidia for hardware donation under CUDA Research Center 2014.
-The authors would like to thank...
