X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/88ca69b0273314e1fb1b061bd1bf75accff40205..b2c6a83cf1333a84907d406502210d9ec4afd3b2:/Heter_paper.tex?ds=sidebyside

diff --git a/Heter_paper.tex b/Heter_paper.tex
index b7e61b8..c6f3021 100644
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -236,26 +236,19 @@ increased~\cite{36}.
 \section{Optimization of both energy consumption and performance}
 \label{sec.compet}
 
-Applying DVFS to lower level not surly reducing the energy consumption to
-minimum level. Also, a big scaling for the frequency produces high performance
-degradation percent. Moreover, by considering the drastically increase in
-execution time of parallel program, the static energy is related to this time
-and it also increased by the same ratio. Thus, the opportunity for gaining more
-energy reduction is restricted. For that choosing frequency scaling factors is
-very important process to taking into account both energy and performance. In
-our previous work~\cite{45}, we are proposed a method that selects the optimal
-frequency scaling factor for an homogeneous cluster, depending on the trade-off
-relation between the energy and performance. In this work we have an
-heterogeneous cluster, at each node there is different scaling factors, so our
-goal is to selects the optimal set of frequency scaling factors,
-$Sopt_1,Sopt_2,\dots,Sopt_N$, that gives the best trade-off between energy
-consumption and performance. The relation between the energy and the execution
-time is complex and nonlinear, Thus, unlike the relation between the performance
+Using the lowest frequency for each processor does not necessarily gives the most energy efficient execution of an application. Indeed, even though the dynamic power is reduced while scaling down the frequency of a processor, its computation power is proportionally decreased and thus the execution time might be drastically increased during which dynamic and static powers are being consumed. Therefore,  it might cancel any gains achieved by scaling down the frequency of all nodes to the minimum  and the overall energy consumption of the application might not be the optimal one. It is not trivial to select the appropriate frequency scaling factor for each processor while considering the characteristics of each processor (computation power, range of frequencies, dynamic and static powers) and the task executed (computation/communication ratio) in order to reduce the overall energy consumption and not significantly increase the execution time. In
+our previous work~\cite{45}, we  proposed a method that selects the optimal
+frequency scaling factor for a homogeneous cluster executing a message passing iterative synchronous application while giving the best trade-off
+ between the energy consumption and the performance for such applications. In this work we are interested in 
+heterogeneous clusters as described above. Due to the heterogeneity of the processors, not one but a  set of scaling factors should be selected and it must  give the best trade-off between energy
+consumption and performance. 
+
+The relation between the energy consumption and the execution
+time for an application is complex and nonlinear, Thus, unlike the relation between the performance
 and the scaling factor, the relation of the energy with the frequency scaling
 factors is nonlinear, for more details refer to~\cite{17}.  Moreover, they are
 not measured using the same metric.  To solve this problem, we normalize the
-execution time by calculating the ratio between the new execution time (the
-scaled execution time) and the old one as follow:
+execution time by computing the ratio between the new execution time (after scaling down the frequencies of some processors) and the initial one (with maximum frequency for all nodes,) as follows:
 \begin{multline}
   \label{eq:pnorm}
   P_\textit{Norm} = \frac{T_\textit{New}}{T_\textit{Old}}\\
@@ -264,7 +257,7 @@ scaled execution time) and the old one as follow:
 \end{multline}
 
 
-By the same way, we are normalize the energy by calculating the ratio between the consumed energy with scaled frequency and the consumed energy without scaled frequency:
+In the same way, we normalize the energy by computing the ratio between the consumed energy while scaling down the frequency and the consumed energy with maximum frequency for all nodes:
 \begin{multline}
   \label{eq:enorm}
   E_\textit{Norm} = \frac{E_\textit{Reduced}}{E_\textit{Original}} \\
@@ -272,11 +265,10 @@ By the same way, we are normalize the energy by calculating the ratio between th
  \sum_{i=1}^{N} {(Ps_i \cdot T_{New})}}{\sum_{i=1}^{N}{( Pd_i \cdot  Tcp_i)} +
  \sum_{i=1}^{N} {(Ps_i \cdot T_{Old})}}
 \end{multline} 
-Where $T_{New}$ and $T_{Old}$ is computed as in EQ(\ref{eq:pnorm}). The second
-problem is that the optimization operation for both energy and performance is
-not in the same direction.  In other words, the normalized energy and the
-normalized execution time curves are not at the same direction.  While the main
-goal is to optimize the energy and execution time in the same time.  According
+Where $T_{New}$ and $T_{Old}$ are computed as in EQ(\ref{eq:pnorm}).
+
+  The normalized energy and execution time curves are not in the same direction.   While the main
+goal is to optimize the energy and execution time at the same time.  According
 to the equations~(\ref{eq:enorm}) and~(\ref{eq:pnorm}), the set of frequency
 scaling factors $S_1,S_2,\dots,S_N$ reduce both the energy and the execution
 time simultaneously.  But the main objective is to produce maximum energy
