correction

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index f5917faea6777f48dae3d38c31be94783ef0dc20..8ada2a0b96a2b372d42beb6546a4207014bc4886 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -675,52 +675,53 @@ The benchmarks have seven different classes, S, W, A, B, C, D and E, that repres
 
 \subsection{The experimental results of the scaling algorithm}
 \label{sec.res}
 
 \subsection{The experimental results of the scaling algorithm}
 \label{sec.res}

-In this section, the scaling factor selection algorithm \ref{HSA}, is applied 
-to NAS parallel benchmarks. Seven benchmarks, CG, MG, EP, LU, BT, SP and FT, of the class D
-are executed over grid'5000 computing clusters. As mentioned previously, the experiments 
-of this paper obtained from a collection of many clusters distributed in two sites, Lyon and Nancy sites, 
-of grid'5000. Four different clusters are selected from these two sites to generate two 
-different scenarios. Each of these two scenarios used three clusters. The first scenario,
-is composed from three clusters that located in two sites, Lyon and Nancy sites. One of these three
-clusters is from Lyon site, Taurus cluster and the other two clusters are form Nancy site, 
-Graphene and Griffon clusters. The second scenario, is composed from three clusters that are 
-located in one site, Nancy site. These cluster are Graphite, Graphene and griffon. The main reason 
-behind using these two scenarios is because the first one is executing the NAS parllel benchmarks over 
-two sites that are connected via long distance network, then the computations to communications ratio 
-is very low due to the increase in communication times, while in the second scenario, all of the three clusters are 
-located in one site and they are connected via high speed local area networks, where the computations 
-to communications ratio is higher. Therefore, it is very interested to know the performance behaviour 
-and the energy consumption of NAS parallel benchmarks using the proposed method, when they run 
-over these two different platform scenarios. Moreover, The NAS parallel benchmarks are executed over 
+In this section, the results of the the application of the scaling factors selection algorithm \ref{HSA} 
+to the NAS parallel benchmarks are presented. 
+
+As mentioned previously, the experiments 
+were conducted over two sites of grid'5000,   Lyon and Nancy sites. 
+Two scenarios were considered while selecting the clusters from these two sites :
+\begin{itemize}
+\item In the first scenario, nodes from two sites and three heterogeneous clusters were selected. The two sites are connected 
+are connected via a long distance network.
+\item In the second scenario nodes from three clusters that are 
+located in one site, Nancy site.  
+\end{itemize}
+
+The main reason 
+behind using these two scenarios is to evaluate the influence of long distance communications (higher latency) on the performance of the 
+scaling factors selection algorithm. Indeed, in the first scenario  the computations to communications ratio 
+is very low due to the higher communication times which reduces the effect of DVFS operations.
+
+The NAS parallel benchmarks are executed over 

 16 and 32 nodes for each scenario. The number of participating computing nodes form each cluster 
 16 and 32 nodes for each scenario. The number of participating computing nodes form each cluster 

-are different, this  depends on the available number of nodes in each cluster. 
-Table \ref{tab:sc} shows the details of these two scenarios and the number of nodes 
-used from each cluster.
+are different because all the selected clusters do not have the same available number of nodes and all benchmarks do not require the same number of computing nodes.
+Table \ref{tab:sc} shows the number of nodes used from each cluster for each scenario. 

 
 \begin{table}[h]
 
 \caption{The different clusters scenarios}
 \centering
 
 \begin{table}[h]
 
 \caption{The different clusters scenarios}
 \centering

-\begin{tabular}{|*{3}{c|}}
+\begin{tabular}{|*{4}{c|}}

 \hline
 \hline

-\multirow{2}{*}{Scenario name}        & \multicolumn{2}{c|} {The participating clusters} \\ \cline{2-3} 
-                                      & Cluster name           & No. of  nodes of each cluster     \\ 
+\multirow{2}{*}{Scenario name}        & \multicolumn{2}{c|} {The participating clusters} \\ \cline{2-4} 
+                                      & Cluster & Site           & No. of  nodes     \\ 

 \hline
 \hline

-\multirow{3}{*}{Two sites / 16 nodes} & Taurus                 & 5                      \\ \cline{2-3} 
-                                      & Graphene               & 5                      \\ \cline{2-3} 
-                                      & Griffon                & 6                      \\ 
+\multirow{3}{*}{Two sites / 16 nodes} & Taurus & Lyon                & 5                      \\ \cline{2-4} 
+                                      & Graphene  & Nancy             & 5                      \\ \cline{2-4} 
+                                      & Griffon       & Nancy        & 6                      \\ 

 \hline
 \hline

-\multirow{3}{*}{Tow sites / 32 nodes} & Taurus                 & 10                     \\ \cline{2-3} 
-                                      & Graphene               & 10                     \\ \cline{2-3} 
-                                      & Griffon                & 12                     \\ 
+\multirow{3}{*}{Tow sites / 32 nodes} & Taurus  & Lyon               & 10                     \\ \cline{2-4} 
+                                      & Graphene  & Nancy             & 10                     \\ \cline{2-4} 
+                                      & Griffon     &Nancy           & 12                     \\ 

 \hline
 \hline

-\multirow{3}{*}{One site / 16 nodes}  & Graphite               & 4                      \\ \cline{2-3} 
-                                      & Graphene               & 6                      \\ \cline{2-3} 
-                                      & Griffon                & 6                      \\ 
+\multirow{3}{*}{One site / 16 nodes}  & Graphite    & Nancy            & 4                      \\ \cline{2-4} 
+                                      & Graphene     & Nancy           & 6                      \\ \cline{2-4} 
+                                      & Griffon         & Nancy        & 6                      \\ 

 \hline
 \hline

-\multirow{3}{*}{One site / 32 nodes}  & Graphite               & 4                      \\ \cline{2-3} 
-                                      & Graphene               & 12                     \\ \cline{2-3} 
-                                      & Griffon                & 12                       \\ 
+\multirow{3}{*}{One site / 32 nodes}  & Graphite   & Nancy             & 4                      \\ \cline{2-4} 
+                                      & Graphene      & Nancy          & 12                     \\ \cline{2-4} 
+                                      & Griffon          & Nancy       & 12                       \\ 

 \hline
 \end{tabular}
  \label{tab:sc}
 \hline
 \end{tabular}
  \label{tab:sc}


@@ -743,23 +744,25 @@ used from each cluster.
 \end{figure}
 
 The NAS parallel benchmarks are executed over these two platform
 \end{figure}
 
 The NAS parallel benchmarks are executed over these two platform

-scenarios with different number of nodes, as in Table \ref{tab:sc}. 
-The overall energy consumption of all benchmark, class D, with 
-applying the proposed frequency selection algorithm is measured 
+ with different number of nodes, as in Table \ref{tab:sc}. 
+The overall energy consumption of all the benchmarks solving the class D instance and
+using the proposed frequency selection algorithm is measured 

 using the equation of the reduced energy consumption, equation 
 (\ref{eq:energy}). This model uses the measured dynamic and static 
 using the equation of the reduced energy consumption, equation 
 (\ref{eq:energy}). This model uses the measured dynamic and static 

-power values that showed in Table \ref{table:grid5000}. The execution
-time is measured for all benchmarks over these different scenarios.  
-The energy consumptions  and the execution times for all benchmarks are 
-demonstrated in the plots \ref{fig:eng_sen} and \ref{fig:time_sen} respectively. 
-In general, the energy consumptions of NAS benchmarks over one site scenario 
-for  16 and 32 nodes are less than those executed over the two sites 
-scenarios. This because in the two sites scenario the communication times 
-are higher, due to long distance communications between the two distributed sites. 
-This leading to more static energy consumption which is linearly related to the 
-increased in the communication time. The execution times of these benchmarks 
-over one sites for 16 and 32 nodes are less  comparing to the two sites 
-scenario according to the increase in communications times.
+power values  showed in Table \ref{table:grid5000}. The execution
+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. 
+ 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 
 
 The EP and MG benchmarks, where there are no or small communications, showed 
 that their execution times and the energy consumptions are not effected 

diff --git a/mpi-energy2-extension/my_reference.bib b/mpi-energy2-extension/my_reference.bib
index de5f5482d2d7f719086bb096e4de7efbbb0cc979..ae351be70b832bdf6e6c035d2f0bd012fc1e9d40 100644 (file)
--- a/mpi-energy2-extension/my_reference.bib
+++ b/mpi-energy2-extension/my_reference.bib
@@ -805,7 +805,6 @@ ISSN={1045-9219},}
   address = {Hyderabad, India},
   booktitle = {PDSEC 2015, 16th IEEE Int. Workshop on Parallel and Distributed Scientific and Engineering Computing (in conjuction with IPDPS 2015)},
   month = {May},
   address = {Hyderabad, India},
   booktitle = {PDSEC 2015, 16th IEEE Int. Workshop on Parallel and Distributed Scientific and Engineering Computing (in conjuction with IPDPS 2015)},
   month = {May},

-  %pages = {***--***},

   publisher = {IEEE}
 }
 
   publisher = {IEEE}
 }