coreection

diff --git a/paper.tex b/paper.tex
index 4533cb9a1f480739f0ec66f645a47175d53d38ac..f48492dd86bbe8d40a243e4b6e8758384f28bcb7 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -109,12 +109,12 @@ In this paper we propose the parallelization of Ehrlich-Aberth method which has
 \item The parallel implementation of Ehrlich-Aberth algorithm on a
   multi-GPU platform with a distributed memory using MPI API, such
   that each GPU is attached and managed by a MPI process. The GPUs
 \item The parallel implementation of Ehrlich-Aberth algorithm on a
   multi-GPU platform with a distributed memory using MPI API, such
   that each GPU is attached and managed by a MPI process. The GPUs

-  exchange their data by message-passing communications.
+  exchange their data by message-passing communications. This approach is more used on clusters to solve very complex problems that are too large for traditional supercomputers, which are very expensive to build and run.

 \item
   Our method is efficient to compute the roots of sparse and full
   polynomials of degree up to 5 millions.
  \end{itemize}
 \item
   Our method is efficient to compute the roots of sparse and full
   polynomials of degree up to 5 millions.
  \end{itemize}

-This latter approach is more used on clusters to solve very complex problems that are too large for traditional supercomputers, which are very expensive to build and run.
+

 
 The paper is organized as follows. In Section~\ref{sec2} we present three different parallel programming models OpenMP, MPI and CUDA. In Section~\ref{sec3} we present the implementation of the Ehrlich-Aberth algorithm on a single GPU. In Section~\ref{sec4} we present the parallel implementations of the Ehrlich-Aberth algorithm on multiple GPUs using the OpenMP and MPI approaches. In section~\ref{sec5} we present our experiments and discuss them. Finally, Section~\ref{sec6} concludes this paper and gives some hints for future research directions in this topic. 
 
 
 The paper is organized as follows. In Section~\ref{sec2} we present three different parallel programming models OpenMP, MPI and CUDA. In Section~\ref{sec3} we present the implementation of the Ehrlich-Aberth algorithm on a single GPU. In Section~\ref{sec4} we present the parallel implementations of the Ehrlich-Aberth algorithm on multiple GPUs using the OpenMP and MPI approaches. In section~\ref{sec5} we present our experiments and discuss them. Finally, Section~\ref{sec6} concludes this paper and gives some hints for future research directions in this topic.