corrected some paragraphs

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index b2c2c21e7a3a5dea5693aac7e66121ceb2fe2195..56b5edd1d7926e5c033d0ce1ac3b53f954dcaf84 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -814,108 +814,105 @@ 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\%.
 
 
 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.
+Figure \ref{fig:per_d} presents the performance degradation percentages for all benchmarks over the two scenarios.

 The performance degradation percentage  for the benchmarks running  on two sites  with
 16 or 32  nodes is on average equal to 8\% or 4\% respectively. 
 The performance degradation percentage  for the benchmarks running  on two sites  with
 16 or 32  nodes is on average equal to 8\% or 4\% respectively. 

+For this scenario, the proposed scaling algorithm selects smaller frequencies for the executions with 32 nodes  without significantly degrading their performance because the communication times are higher with 32 nodes which results in smaller  computations to communications ratio.  On the other hand, 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. In opposition to the two sites scenario, when the number of computing nodes is increased in the one site scenario, the performance degradation percentage is increased. Therefore, doubling the number of computing 
+nodes when the communications occur in high speed network does not decrease the computations to 
+communication ratio. 

 
 

- \textcolor{red}{ 
-The proposed scaling algorithm selecting smaller frequencies in two sites scenario,
-due to decreasing in the computations to communications ratio when the number of nodes is increased and
-leads to less performance degradation percentage.  
-In contrast, 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. 
-The inverse is happens in this scenario when the number of computing nodes is increased 
-the performance degradation percentage is decreased. So, using double number of computing 
-nodes when the communications occur in high speed network not decreased the computations to 
-communication ratio. Moreover, as shown in the figure \ref{fig:time_sen}, the execution time of one site scenario with 32 nodes 
-are less by approximately double, linear speed-up, for most of the benchmarks comparing to the one site with 16 nodes scenario. 
+
+ Figure \ref{fig:time_sen} presents the execution times for all the benchmarks over the two scenarios. For most of the benchmarks running over the one site scenario, their execution times are approximately divided by two  when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes).  
+ 

 This leads to increased the number of the critical nodes which any one of them may increased the overall the execution time of the benchmarks.
 This leads to increased the number of the critical nodes which any one of them may increased the overall the execution time of the benchmarks.

-The EP benchmarks is gives the bigger performance degradation ratio, because there is no 
-communications and no slack times in this benchmarks which their performance controlled by 
-the computing powers of the nodes.
-The tradeoff between these scenarios can be computed as in the tradeoff function \ref{eq:max}.
-Figure \ref{fig:dist}, presents the tradeoff distance for all benchmarks  over all 
-platform scenarios.  The one site scenario with 16 and 32 nodes had the best tradeoff distance 
-compared to the two sites scenarios, due to the increase or decreased in the communications as mentioned before.
-The one site scenario with 16 nodes is the best scenario in term of energy and performance tradeoff,
-which on average is up 26\%.  Therefore, the tradeoff distance is related linearly to the  energy saving  
-percentage. Finally, the best energy and performance tradeoff depends on the all of the following:
-1) the computations to communications ratio when there is a communications and slack times, 2) the differences in computing powers 
-between the computing nodes and 3) the differences in static and the dynamic powers of the nodes.}
+The EP benchmark gives bigger performance degradation percentage, because there is no 
+communications and no slack times in this benchmark which their performance controlled by 
+the computing powers of the nodes. \textcolor{red}{les deux phrases précédentes n'ont pas de sens}
+

 
 

+Figure \ref{fig:dist} presents the  distance between the energy consumption reduction and the performance degradation for all benchmarks  over both  scenarios. This distance  can be computed as in the tradeoff function \ref{eq:max}. The one site scenario with 16 and 32 nodes had the best tradeoff distance compared to the two sites scenarios, due to the increase or decreased in the communications as mentioned before. The one site scenario with 16 nodes gives the best energy and performance tradeoff which is on average equal to  26\%. \textcolor{red}{distance is a percentage}

 
 

+ Therefore, the tradeoff distance is linearly related  to the  energy saving  
+percentage. Finally, the best energy and performance tradeoff depends on  all of the following:
+1) the computations to communications ratio when there are  communications and slack times, 2) the heterogeneity of the computing powers of the nodes and 3) the heterogeneity of the consumed  static and dynamic powers of the nodes.
+
+\textcolor{red}{compare the two scenarios}

 
 \subsection{The experimental results of multicores clusters}
 \label{sec.res-mc}
 
 \subsection{The experimental results of multicores clusters}
 \label{sec.res-mc}

-The grid'5000 clusters have different number of cores embedded in their nodes
-as in the Table \ref{table:grid5000}. Moreover, the cores of each node are 
-connected via shared memory model, the data transfer between cores' local 
-memories achieved via the global memory \cite{rauber_book}. Therefore, in 
-this section the proposed scaling algorithm is implemented over the  grid'5000 
-clusters which are included multicores in the selected nodes as same as the 
-two previous platform scenarios that mentioned in the section \ref{sec.res}.
-The two platform scenarios, the two sites and one site scenarios,  with 32 
-nodes are reconfigured to used multicores for each node. For example if 
-the participating number of nodes from a certain cluster is equal to 12 nodes, 
-in the multicores scenario the selected nodes is equal to 3 nodes with using 
-4 cores for each of them to produced 12 cores. These scenarios with one  
-core and  multicores are  demonstrated in Table \ref{table:sen-mc}. 
+The  clusters of grid'5000 have different number of cores embedded in their nodes
+as shown in Table \ref{table:grid5000}. The cores of each node can exchange 
+ data via the shared memory \cite{rauber_book}. In 
+this section, the proposed scaling algorithm is evaluated over the grid'5000 grid while using multi-core nodes 
+selected according to the two  platform scenarios described in the section \ref{sec.res}.
+The two platform scenarios, the two sites and one site scenarios, use  32 
+cores from multi-cores nodes instead of 32 distinct nodes. For example if 
+the participating number of cores from a certain cluster is equal to 12, 
+in the multi-core scenario the selected nodes is equal to 3 nodes while using 
+4 cores from each node. The platforms with one  
+core per node and  multi-cores nodes are  shown in Table \ref{table:sen-mc}. 

 The energy consumptions and execution times of running the NAS parallel 
 The energy consumptions and execution times of running the NAS parallel 

-benchmarks, class D, over these four different scenarios are represented 
+benchmarks, class D, over these four different scenarios are presented 

 in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.
 in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.

-The execution times of NAS  benchmarks over the one site multicores scenario 
-is higher than the execution time of those running over one site multicores scenario. 
-The reason in the one site multicores scenario the communication is increased significantly,
-and all node's cores  share  the same node network link which increased 
-the communication times. Whereas, the execution times of the NAS benchmarks  over 
-the two site  multicores scenario is less than those executed over the two 
-sites one core scenario. This goes back when using multicores is decreasing the communications. 
+
+The execution times for most of  the NAS  benchmarks are higher over the one site multi-cores per node scenario 
+ than the execution time of those running over one site single core per node  scenario. Indeed,  
+   the communication times  are higher in the one site multi-cores scenario than in the latter scenario because all the cores of a node  share  the same node network link which can be  saturated when running communication bound applications. On the other hand,  the execution times for most of the NAS benchmarks  are lower over 
+the two site  multi-cores scenario than those over the two sites one core scenario. 
+
+This goes back when using multicores is decreasing the communications. 

 As explained previously, the cores shared same nodes' linkbut  the communications between the cores 
 are still less than the communication times between the nodes over the long distance 
 As explained previously, the cores shared same nodes' linkbut  the communications between the cores 
 are still less than the communication times between the nodes over the long distance 

-networks, and thus the over all execution time decreased. Generally, executing 
-the NAS benchmarks over the one site one core scenario gives smaller execution times 
-comparing to other scenarios. This due to each node in this scenario has it's 
-dedicated network link that used independently by one core, while in the other 
-scenarios the communication times are higher when using long distance communications 
-link or using the  shared link communications between cores of each node. 
-On the other hand, the energy consumptions of the NAS benchmarks over the 
-one site one cores is less than the one site multicores scenario because 
-this scenario had less execution time as mentioned before. Also, in the 
-one site one core scenario the computations to communications ratio is 
-higher, then the new scaled frequencies are decreased the dynamic energy 
+networks, and thus the over all execution time decreased. \textcolor{red}{this is not true}
+
+The experiments showed that for most of the NAS benchmarks and between the four scenarios,  the one site one core scenario gives the best execution times because the communication times are the lowest. Indeed, in this scenario each core has a dedicated network link and all the communications are local.  
+Moreover, the energy consumptions of the NAS benchmarks  are lower over the 
+one site one core scenario  than over the one site multi-cores scenario because 
+the first scenario had less execution time than the latter which results in less static energy being consumed. 
+The computations to communications ratios of the NAS benchmarks are higher over the one site one core scenario  than the other scenarios \textcolor{red}{ then the new scaled frequencies are decreased the dynamic energy 

 consumption which is decreased exponentially 
 consumption which is decreased exponentially 

-with the new frequency scaling factors. These experiments also showed, the energy 
-consumption and the execution times of EP and MG benchmarks over these four 
-scenarios are not change a lot, because there are no or small communications 
-which are increase or decrease the static power consumptions. 
-The other benchmarks were showed that their energy consumptions and execution times 
-are changed according to the decreasing or increasing in the communication 
-times that are different from scenario to other or due to the amount of 
-communications in each of them.
-
-The energy saving percentages of all NAS benchmarks, as in figure 
-\ref{fig:eng-s-mc}, running over these four scenarios are presented. The figure 
-showed the energy saving percentages of NAS benchmarks over two sites multicores scenario is higher 
-than two sites once core scenario, because  the computation 
-times in this scenario is higher than the other one, then the more reduction in the 
-dynamic energy can be obtained as mentioned previously. In contrast, in the one site one 
-core and one site multicores scenarios the energy saving percentages 
-are approximately equivalent, on average they are up to 25\%. In these  both scenarios there are a small difference  in the 
-computations to communications ratio, leading the proposed scaling algorithm 
-to selects the frequencies proportionally to these ratios and keeping 
-as much as possible the energy saving percentages the same. The 
-performance degradation percentages of NAS benchmarks are presented in
-figure \ref{fig:per-d-mc}. This figure indicates that performance 
-degradation percentages of running NAS benchmarks over two sites 
-multocores scenario, on average is equal to 7\%,  gives more performance degradation percentage
-than two sites  one core scenario, which on average is equal to 4\%. 
-Moreover, using the two sites multicores scenario increased 
-the computations to communications ratio, which may be increased 
-the overall execution time  when the proposed scaling algorithm is applied and scaling down the frequencies.  
-The inverse was happened when the benchmarks are executed  over one 
+with the new frequency scaling factors. I do not understand this sentence}
+\textcolor{red}{It is useless to use multi-cores then!}
+
+
+  These experiments also showed that the energy 
+consumption and the execution times of the EP and MG benchmarks do not change significantly over these four 
+scenarios  because there are no or small communications 
+which could increase or decrease the static power consumptions. Contrary to EP and MG, the  energy consumptions and the execution times of the rest of the  benchmarks  vary according to the  communication 
+times that are different from one scenario to the other.
+
+The energy saving percentages of all NAS benchmarks running over these four scenarios are presented in figure \ref{fig:eng-s-mc}. The figure 
+shows that  the energy saving percentages are higher  over the two sites multi-cores scenario 
+than over the two sites one core scenario, because  the computation 
+times  are higher in the first scenario than in the latter, thus, more dynamic energy can be saved by applying the frequency scaling algorithm. \textcolor{red}{why the computation times are higher!}
+
+
+In contrast, in the one site one 
+core and one site multi-cores scenarios the energy saving percentages 
+are approximately equivalent, on average they are up to 25\%. In both scenarios there are a small difference  in the 
+computations to communications ratios which leads the proposed scaling algorithm 
+to select similar frequencies for both scenarios.  
+
+The 
+performance degradation percentages of the NAS benchmarks are presented in
+figure \ref{fig:per-d-mc}. 
+
+It indicates that the performance 
+degradation percentages for the NAS benchmarks are higher over the two sites 
+multi-cores scenario than over the  two sites  one core scenario, equal on average to 7\% and 4\% respectively. 
+Moreover, using the two sites multi-cores scenario increased 
+the computations to communications ratio, which may increase 
+the overall execution time  when the proposed scaling algorithm is applied and  the frequencies scaled down.  
+
+
+When the benchmarks are executed  over the one 

 site one core scenario their performance degradation percentages, on average
 site one core scenario their performance degradation percentages, on average

-is equal to 10\%, are higher than those executed over one sit one core, 
-which on average is equal to 7\%. So, in one site 
+is equal to 10\%, are higher than those executed over one site one core, 
+which on average is equal to 7\%. \textcolor{red}{You are comparing the  one 
+site one core scenario to itself! Please rewrite all the following paragraphs because they are full of mistakes! Look how I modified the previous parts, discover your mistakes and stop making the same mistakes.}
+
+So, in one site 

 multicores scenario the computations to communications ratio is decreased
 as mentioned before, thus selecting new frequencies are not increased 
 the overall execution time. The tradeoff distances of all NAS 
 multicores scenario the computations to communications ratio is decreased
 as mentioned before, thus selecting new frequencies are not increased 
 the overall execution time. The tradeoff distances of all NAS 


@@ -928,7 +925,8 @@ has the same  energy saving percentages of the one site one core scenario but
 with less performance degradation. The two sites multicores scenario is gives better 
 energy and performance tradeoff, one average is equal to 14.7\%, than the two sites
 one core, on average is equal to 13.3\%.
 with less performance degradation. The two sites multicores scenario is gives better 
 energy and performance tradeoff, one average is equal to 14.7\%, than the two sites
 one core, on average is equal to 13.3\%.

-Finally, using multicore in both scenarios increased the energy and performance tradeoff 
+
+Finally, using multi-cores in both scenarios increased the energy and performance tradeoff 

 distance. This generally due to using multicores was increased the computations to communications 
 ratio in two sites scenario and thus the energy saving percentage increased over the performance degradation percentage, whereas  this ratio was decreased  
 in one site  scenario causing the performance degradation percentage decreased over the energy saving percentage.
 distance. This generally due to using multicores was increased the computations to communications 
 ratio in two sites scenario and thus the energy saving percentage increased over the performance degradation percentage, whereas  this ratio was decreased  
 in one site  scenario causing the performance degradation percentage decreased over the energy saving percentage.