X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/7851a05d8c696e72f32cc2b36170ff31a213bfc1..e190696e80767b1bc629701f186939c868d24395:/mpi-energy2-extension/Heter_paper.tex?ds=inline

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index f5917fa..8ada2a0 100644
--- 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}
-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 
-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{tabular}{|*{3}{c|}}
+\begin{tabular}{|*{4}{c|}}
 \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
-\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
-\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
-\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
-\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}
@@ -743,23 +744,25 @@ used from each cluster.
 \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 
-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 
