X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/c911c7db174d7bc2cd2a2893deceb956fff0e02b..9bf808f8e110db33a501c0202fb6470145106354:/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 7969c4b..6b3fbb1 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -192,7 +192,8 @@
    }
 
 \begin{abstract}
-  In recent years, green computing topic  has  become an important topic 
+
+  In recent years, green computing   has  become an important topic 
   in the supercomputing research domain. However, the 
   computing platforms are still  consuming more and
 more energy due to the increasing number of nodes composing
@@ -220,8 +221,8 @@ scaling (DVFS) is one of them. It can be used to reduce the power consumption of
 
 
 \begin{keyword}
-\textcolor{blue}{
-DVFS \sep heterogeneous grid \sep energy consumption \sep performance prediction  \sep energy and performance trade-off  \sep frequencies selecting algorithm }
+
+Dynamic voltage and frequency scaling \sep Grid computing\sep Green computing and  frequency scaling online algorithm.
 
 %% keywords here, in the form: keyword \sep keyword
 
@@ -1100,20 +1101,20 @@ which increase the computations to communications ratio  and the latter uses lon
 
 
 
-\subsection{The experimental results of multi-cores clusters}
+\subsection{The experimental results over multi-cores clusters}
 \label{sec.res-mc}
-\textcolor{blue}{
+
 The  clusters of grid'5000 have different number of cores embedded in their nodes
 as shown in Table \ref{table:grid5000}. In 
-this section, the proposed scaling algorithm is evaluated over the grid'5000 grid while using multi-core nodes selected according to the one site scenario described in the section \ref{sec.res}.
-The one site scenario, uses  32 cores from multi-cores nodes instead of 32 distinct nodes. For example if 
+this section, the proposed scaling algorithm is evaluated over the  grid'5000 platform  while using multi-cores nodes selected according to the one site scenario described in the section \ref{sec.res}.
+The one site scenario uses  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 14, 
 in the multi-core scenario the selected nodes is equal to  4 nodes while using 
 3 or 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 
-benchmarks, class D, over these four different scenarios are presented 
-in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.}
+The energy consumptions and execution times of running the class D of the NAS parallel 
+benchmarks over these four different scenarios are presented 
+in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.
 
 \begin{table}[]
 \centering
@@ -1122,10 +1123,10 @@ in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.}
 \hline
 Scenario name                          & Cluster name & \begin{tabular}[c]{@{}c@{}}No. of  nodes\\ in each cluster\end{tabular} & 
                                        \begin{tabular}[c]{@{}c@{}}No. of  cores\\ for each node\end{tabular}  \\ \hline
-\multirow{3}{*}{One site/ one core}    & Graphite     & 4               & 1                   \\  \cline{2-4}
+\multirow{3}{*}{One core per node}    & Graphite     & 4               & 1                   \\  \cline{2-4}
                                        & Graphene     & 14              & 1                   \\  \cline{2-4}
                                        & Griffon      & 14              & 1                   \\ \hline
-\multirow{3}{*}{One site/ multicores}  & Graphite     & 1               &  4              \\  \cline{2-4}
+\multirow{3}{*}{Multi-cores per node}  & Graphite     & 1               &  4              \\  \cline{2-4}
                                        & Graphene     & 4               & 3 or 4              \\  \cline{2-4}
                                        & Griffon      & 4               & 3 or 4                   \\ \hline
 \end{tabular}
@@ -1140,39 +1141,36 @@ Scenario name                          & Cluster name & \begin{tabular}[c]{@{}c@
   \subfloat[Comparing the  energy consumptions of running NAS benchmarks over one core and multi-cores scenarios]{%
     \includegraphics[width=.4\textwidth]{fig/eng_con.eps}\label{fig:eng-cons-mc}}
     \label{fig:eng-cons}
-  \caption{The energy consumptions and execution times of NAS benchmarks over one core and multi-cores scenarios}
+  \caption{The energy consumptions and execution times of NAS benchmarks over one core and multi-cores per node architectures}
 \end{figure}
 
 
-\textcolor{blue}{
-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 execution times for most of  the NAS  benchmarks are higher over the multi-cores per node scenario 
+than over 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. Moreover, the cores of a node share the memory bus which can be also saturated and become a bottleneck.    
-The experiments showed that for most of the NAS benchmarks,      
-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 memory bus.  
 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 
+ one core scenario  than over the 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  when compared to the ratio of the multi-cores scenario. 
-More energy reduction was achieved when this ratio is increased because the proposed scaling algorithm selects smaller frequencies that decrease the dynamic power consumption. 
-These experiments also showed that the energy 
+More energy reduction can be gained when this ratio is big because it pushes the proposed scaling algorithm to select smaller frequencies that decrease the dynamic power consumption. These experiments also showed that the energy 
 consumption and the execution times of the EP and MG benchmarks do not change significantly over these two
-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 two scenarios are presented in the figure \ref{fig:eng-s-mc}. It shows that  the energy saving percentages in the one site one 
-core and one site multi-cores scenarios
-are approximately equivalent, on average they are equal to  25.9\% and 25.1\% respectively. 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.  
+scenarios  because there are no or small communications. 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 two scenarios are presented in the figure \ref{fig:eng-s-mc}. 
+The figure shows that  the energy saving percentages in the one 
+core and the multi-cores scenarios
+are approximately equivalent, on average they are equal to  25.9\% and 25.1\% respectively.
+The energy consumption is reduced at the same rate in the two scenarios when compared to the energy consumption of the executions without DVFS. 
+
+
 The performance degradation percentages of the NAS benchmarks are presented in
-figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages for the NAS benchmarks over one site one core is on average equal to 10.6\% and is higher than these executed over the one site multi-cores scenario, which is on average equal to 7.5\%. 
-The performance degradation percentages over one site multi-cores is lower because  the computations to communications ratio is decreased. Therefore, selecting big
-frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.
+figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages is higher for the NAS benchmarks over the  one core per node scenario  (on average equal to 10.6\%)  than over the  multi-cores scenario (on average equal to 7.5\%). The performance degradation percentages over the multi-cores scenario is lower because  the computations to communications ratio is smaller than the ratio of the other scenario. 
+
 The tradeoff distance percentages of the NAS benchmarks over the two scenarios are presented 
-in the figure \ref{fig:dist-mc}. 
-These  tradeoff distance percentages are used to verify which scenario is the best in terms of energy reduction and performance. The figure shows that using muti-cores  scenario gives bigger tradeoff distance percentages, on overage equal to 17.6\%  than using one core per node scenario,  on average  equal to 15.3\%.}
+in the figure \ref{fig:dist-mc}. These  tradeoff distance between energy consumption reduction and performance  are used to verify which scenario is the best in both terms  at the same time. The figure shows that  the  tradeoff distance percentages are on average   bigger over the multi-cores scenario  (17.6\%) than over the  one core per node scenario  (15.3\%).
 
 
 
@@ -1331,4 +1329,4 @@ supporting his work.
 
 \end{document}
 
-%% End of file `ecrc-template.tex'. 
+
