relecture de l'abstract

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 \usepackage[textsize=footnotesize]{todonotes}
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 % As a general rule, do not put math, special symbols or citations
 % in the abstract
 \begin{abstract}
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 % in the abstract
 \begin{abstract}

-Finding roots of polynomials is a well-known important but not so very easy problem to solve especially for high degrees. \LZK{il manque quelque chose dans cette phrase. Proposition: Finding roots of polynomials is a very important part of solving real-life problems but it is not an easy task for polynomials of high degrees.}
-In this paper, we present two different parallel approaches to achieve this gaol for sparse and fully defined polynomials of up to 1.4 billion degree. Our two approaches are based on the well known parallel paradigms of OpenMP and MPI but combined with the novel CUDA GPU technology. Our results show a quasi-linear speedup using up to 4 GPU devices to solve four times faster a polynomial root finding problem. To our knowledge, this is the first paper to present this technology mix to solve such a highly demanding problem in parallel programming.
+\LZK{J'ai un peu modifié l'abstract. Sinon à revoir pour le degré max des polynômes testés après les tests de raph.}
+Finding roots of polynomials is a very important part of solving real-life problems but it is not so easy for polynomials of high degrees. In this paper, we present two different parallel algorithms of the Ehrlich-Aberth method to find roots of sparse and fully defined polynomials of high degrees. Both algorithms are based on CUDA technology to be implemented on multi-GPU computing platforms but each using different parallel paradigms: OpenMP or MPI. The experiments show a quasi-linear speedup by using up-to 4 GPU devices to find roots of polynomials of degree up-to 1.4 billion. To our knowledge, this is the first paper to present this technology mix to solve such a highly demanding problem in parallel programming. \LZK{Je n'ai pas bien saisi la dernière phrase.}

 
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