X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/569691a986500cc672299c47d7823b8cc88c8f7b..33d8c5cc285296a345d562d9890cff0b6eac7fe6:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index 384ff8b..f75c983 100644
--- a/paper.tex
+++ b/paper.tex
@@ -655,7 +655,6 @@ significant).
 
 
 
-%%HIER END MY REVISIONS (SIDER)
 \section{Experimental study}
 \label{sec6}
 %\subsection{Definition of the used polynomials }
@@ -724,14 +723,13 @@ on the GPU is faster than those implemented on the CPU.
 
 This is due to the GPU ability to compute the data-parallel functions
 faster than its CPU counterpart. However, the execution time for the
-CPU (4 cores) implementation exceed 5,000 s for 250,000 degrees
-polynomials, in counterpart the GPU implementation for the same
-polynomials not reach 100 s, more than again, with an execution time
-under to 2,500 s CPU (4 cores) implementation can resolve With the GPU
-we can solve very high degrees polynomials very quickly up to degree
+CPU (4 cores) implementation exceed 5,000s for 250,000 degrees
+polynomials. In counterpart, the GPU implementation for the same
+polynomials do not take more 100s. With the GPU
+we can solve high degrees polynomials very quickly up to degree
 of 1,000,000. We can also notice that the GPU implementation are
 almost 47 faster then those implementation on the CPU (4
-cores). However the CPU(4 cores) implementation are almost 4 faster
+cores). However the CPU (4 cores) implementation are almost 4 faster
 then his implementation on CPU (1 core). Furthermore, the number of
 iterations and the convergence precision are similar with the CPU
 and the GPU implementation.
