modif Intro, partie commenté par RC et LZK

[kahina_paper2.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index c60847974685f409aeafd328b8ba2ff9cfddb975..8203ed0ec7a6a605d6f076d48d51756fc13dbe1c 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -50,7 +50,7 @@ of polynomials of degree up-to 5 millions.
 \end{abstract}
 
 % no keywords
 \end{abstract}
 
 % no keywords

-\LZK{Faut pas mettre des keywords?}
+\LZK{Faut pas mettre des keywords?\KG{Oui d'après ça: "no keywords" qui se trouve dans leur fichier source!!, mais c'est Bizzard!!!}}

 
 
 \IEEEpeerreviewmaketitle
 
 
 \IEEEpeerreviewmaketitle


@@ -88,17 +88,15 @@ which each processor continues to update its approximations even
 though the latest values of other approximations $z^{k}_{i}$ have not
 been received from the other processors, in contrast with synchronous
 algorithms where it would wait those values before making a new
 though the latest values of other approximations $z^{k}_{i}$ have not
 been received from the other processors, in contrast with synchronous
 algorithms where it would wait those values before making a new

-iteration. Couturier et al.~\cite{Raphaelall01} proposed two methods
+iteration. Couturier and al.~\cite{Raphaelall01} proposed two methods

 of parallelization for a shared memory architecture with OpenMP and
 for a distributed memory one with MPI. They are able to compute the
 of parallelization for a shared memory architecture with OpenMP and
 for a distributed memory one with MPI. They are able to compute the

-roots of sparse polynomials of degree 10,000 in 116 seconds with
-OpenMP and 135 seconds with MPI only by using 8 personal computers and
-2 communications per iteration. The authors showed an interesting
-speedup comparing to the sequential implementation which takes up-to
-3,300 seconds to obtain same results. 
+roots of sparse polynomials of degree 10,000. The authors showed an interesting
+speedup that is 20 times as fast as the sequential implementation. 
+%which takes up-to 3,300 seconds to obtain same results. 

 \RC{si on donne des temps faut donner le proc, comme c'est vieux à mon avis faut supprimer ca, votre avis?} 
 \LZK{Supprimons ces détails et mettons une référence s'il y en a une}
 \RC{si on donne des temps faut donner le proc, comme c'est vieux à mon avis faut supprimer ca, votre avis?} 
 \LZK{Supprimons ces détails et mettons une référence s'il y en a une}

-
+\KG{Je viens de supprimer les détails, la référence existe déja, a reverifier}

 Very few work had been performed since then until the appearing of the Compute Unified Device Architecture (CUDA)~\cite{CUDA15}, a parallel computing platform and a programming model invented by NVIDIA. The computing power of GPUs (Graphics Processing Units) has exceeded that of traditional processors CPUs. However, CUDA adopts a totally new computing architecture to use the hardware resources provided by the GPU in order to offer a stronger computing ability to the massive data computing. Ghidouche et al.~\cite{Kahinall14} proposed an implementation of the Durand-Kerner method on a single GPU. Their main results showed that a parallel CUDA implementation is about 10 times faster than the sequential implementation on a single CPU for sparse polynomials of degree 48,000.
 
 In this paper we propose the parallelization of Ehrlich-Aberth method which has a good convergence and it is suitable to be implemented in parallel computers. We use two parallel programming paradigms OpenMP and MPI on CUDA multi-GPU platforms. Our CUDA-MPI and CUDA-OpenMP codes are the first implementations of Ehrlich-Aberth method with multiple GPUs for finding roots of polynomials. Our major contributions include:
 Very few work had been performed since then until the appearing of the Compute Unified Device Architecture (CUDA)~\cite{CUDA15}, a parallel computing platform and a programming model invented by NVIDIA. The computing power of GPUs (Graphics Processing Units) has exceeded that of traditional processors CPUs. However, CUDA adopts a totally new computing architecture to use the hardware resources provided by the GPU in order to offer a stronger computing ability to the massive data computing. Ghidouche et al.~\cite{Kahinall14} proposed an implementation of the Durand-Kerner method on a single GPU. Their main results showed that a parallel CUDA implementation is about 10 times faster than the sequential implementation on a single CPU for sparse polynomials of degree 48,000.
 
 In this paper we propose the parallelization of Ehrlich-Aberth method which has a good convergence and it is suitable to be implemented in parallel computers. We use two parallel programming paradigms OpenMP and MPI on CUDA multi-GPU platforms. Our CUDA-MPI and CUDA-OpenMP codes are the first implementations of Ehrlich-Aberth method with multiple GPUs for finding roots of polynomials. Our major contributions include: