X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/e190696e80767b1bc629701f186939c868d24395..ec0705e8f0a3472c0bdd894bf3348ece04a9bea3:/mpi-energy2-extension/Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 8ada2a0..7252e44 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -755,21 +755,17 @@ time is measured for all the benchmarks over these different scenarios.
 The energy consumptions  and the execution times for all the benchmarks are 
 presented in the plots \ref{fig:eng_sen} and \ref{fig:time_sen} respectively. 
 
-In general, the energy consumed while executing  the NAS benchmarks over one site scenario 
-for  16 and 32 nodes is lower than the energy consumed while executing over the two sites. 
-The long distance communications between the two distributed sites increases the idle time which leads to more static energy consumption. 
+For the majority of the benchmarks, the energy consumed while executing  the NAS benchmarks over one site scenario 
+for  16 and 32 nodes is lower than the energy consumed while using two sites. 
+The long distance communications between the two distributed sites increase the idle time which leads to more static energy consumption. 
  The execution times of these benchmarks 
 over one site with 16 and 32 nodes are also lower when  compared to those of the  two sites 
 scenario.
 
 
 
-The EP and MG benchmarks, where there are no or small communications, showed 
-that their execution times and the energy consumptions are not effected 
-significantly in both scenarios and when the number of nodes is increase, 
-while the other benchmarks showed the inverse, because they have more communications 
-that proportionally increase the communication times if there are slow 
-communications or using more number of nodes or both of them.
+However, the  execution times and the energy consumptions of EP and MG benchmarks, which have no or small communications, are not significantly affected 
+ in both scenarios. Even when the number of nodes is doubled. On the other hand, the communications of the rest of the benchmarks increases when using long distance communications between two sites or increasing the number of computing nodes.
 
 \begin{figure}
   \centering
@@ -798,38 +794,40 @@ 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. 
-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 
+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. 
+
+
+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 
