X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/75950bb1ef00d4b47e757a4f3e0611b73e0ad398..0fc832a80fa364a4c6ae30d9431e161e5e65e905:/mpi-energy2-extension/Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 8344ac8..3641120 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -794,19 +794,17 @@ The energy saving percentage is computed as the ratio between the reduced
 energy consumption, equation (\ref{eq:energy}), and the original energy consumption,
 equation (\ref{eq:eorginal}), for all benchmarks as in figure \ref{fig:eng_s}. 
 This figure shows that the energy saving percentages of one site scenario for
-16 and 32 nodes are bigger than those of the two sites scenario. This is because
-the computations to communications ratio in  one site scenario is higher 
-than the ratio of the two sites scenarios, due to the increase in the communication
-times. Moreover, the frequencies selecting algorithm selects smaller frequencies, bigger
-scaling factors, when the computations times are higher than communication times, 
-producing  smaller energy consumption, because the dynamic energy consumption 
-is decreased linearly with computation times that decreased exponentially with 
-scaling factors. On the other side, the increase in the number of computing nodes can be 
-increase the communication times and thus producing less energy saving depending on the 
-benchmarks being executed. The benchmarks CG, MG, BT and FT show more 
-energy saving percentage in one site scenario when executed  over 16 nodes comparing to 32 nodes. While 
-the benchmarks  LU and SP showed the inverse, because there computations to 
-communications ratio is not effected to the increase in local site communications. 
+16 and 32 nodes are bigger than those of the two sites scenario which is due
+to the higher  computations to communications ratio in the first scenario   
+than in the second one. Moreover, the frequency selecting algorithm selects smaller frequencies when the computations times are higher than the communication times which 
+results in  a lower energy consumption. Indeed, the dynamic  consumed power
+is exponentially related to the CPU's frequency value. On the other side, the increase in the number of computing nodes can 
+increase the communication times and thus produces less energy saving depending on the 
+benchmarks being executed. The results of the benchmarks CG, MG, BT and FT show more 
+energy saving percentage in one site scenario when executed  over 16 nodes comparing to 32 nodes. While, LU and SP consume more energy with 16 nodes than 32 in one site  because there computations to 
+communications ratio is not affected by the increase of the number of local communications. 
+
+
 While all benchmarks are effected by the long distance communications in the two sites 
 scenarios, except  EP benchmarks. In EP benchmark  there is no communications 
 in their iterations, then it is independent from the effect of local and long