X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/0fc832a80fa364a4c6ae30d9431e161e5e65e905..ec0705e8f0a3472c0bdd894bf3348ece04a9bea3:/mpi-energy2-extension/Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 3641120..7252e44 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -805,25 +805,29 @@ energy saving percentage in one site scenario when executed  over 16 nodes compa
 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 
-distance communications. Therefore, the energy saving percentage of this benchmarks is 
-depend on differences between the computing powers of the computing nodes, for example 
+The energy saving percentage is reduced for all the benchmarks because of the long distance communications in the two sites 
+scenario, except for the   EP benchmark which has  no communications. Therefore, the energy saving percentage of this benchmark is 
+dependent on the maximum difference between the computing powers of the heterogeneous computing nodes, for example 
 in the one site scenario, the graphite cluster is selected but in the two sits scenario 
-this cluster is replaced with Taurus cluster that be more powerful in computing power. 
+this cluster is replaced with Taurus cluster which is more powerful. 
 Therefore, the energy saving of EP benchmarks are bigger in the two site scenario due 
-to increase in the differences between the computing powers of the nodes. This means, the higher 
+to the higher maximum difference between the computing powers of the nodes. 
+In fact,  high
 differences between the nodes' computing powers make the proposed frequencies selecting  
-algorithm  to selects smaller frequencies in the nodes of the higher computing power, 
-producing less energy consumption and thus more energy saving.
-The best energy saving percentage was for one site scenario with 16 nodes, on average it
-saves the energy consumption up to 30\%. 
-
-Figure \ref{fig:per_d}, presents the performance degradation percentages for all benchmarks.
-It shows that the performance degradation percentages  of the one site scenario with
-32 nodes, on average equal to 10\%, is higher than the performance degradation of one 16 nodes, 
-which on average equal to 3\%. This because selecting smaller frequencies in the one site scenarios,
+algorithm  select smaller frequencies for the powerful nodes which 
+produces less energy consumption and thus more energy saving.
+The best energy saving percentage was obtained in the one site scenario with 16 nodes, The energy consumption was on average reduced up to 30\%.
+
+
+Figure \ref{fig:per_d} presents the performance degradation percentages for all benchmarks.
+The performance degradation percentage  for the benchmarks running  on one site  with
+16 or 32  nodes is on average equal to 3\% or 10\% respectively. 
+
+ \textcolor{red}{please correct the following paragraph because I do not understand it at all! Stop using we, this because, effected, while, ...}
+ 
+ 
+ 
+   This because selecting smaller frequencies in the one site scenarios,
 when the computations grater than the communications , increase the number of the critical nodes 
 when the number of nodes increased. The inverse happens in the tow sites scenario,
 this due to the lower computations to communications ratio that decreased with highest