X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper2.git/blobdiff_plain/329c650efca3cd8d8d10932efa575b528cf2d8bf..da52af7b1337d18b03519d0d970824ef617387cf:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index e826d2d..25a169d 100644
--- a/paper.tex
+++ b/paper.tex
@@ -1099,11 +1099,12 @@ sparse and full polynomials ranging from 1,000,000 to 5,000,000.
 \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 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.
+and can solve very high degree polynomials. In addition, with full polynomial as well as sparse ones, both
+approaches give very similar results.
+
+%SIDER JE viens de virer \c ca For sparse polynomials here 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.
  
