X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/e25948dd52ceacdc4dc6175e899ae810ea4cc134..83482b6bebc94e878b3aa52c290509321fdca09c:/Heter_paper.tex?ds=sidebyside

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 90793bf..a88d174 100644
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -260,7 +260,7 @@ where $TcpOld_i$ is the computation time  of processor $i$ during the first
 iteration and $MinTcm$ is the communication time of the slowest processor from 
 the first iteration.  The model computes the maximum computation time 
 with scaling factor from each node  added to the communication time of the \subsection{The verifications of the proposed method}
-\label{sec.verif}
+\label{sec.verif.method}
 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 
@@ -286,9 +286,9 @@ Therefore, the execution time of the iterative application is
 equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied 
 by the number of iterations of that application.
 
-This prediction model is developed from our model for predicting the execution time of 
+This prediction model is developed from the model for predicting the execution time of 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 
-The execution time prediction model is used in our method for optimizing both 
+The execution time prediction model is used in the method for optimizing both 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
@@ -481,7 +481,7 @@ Then, the objective function can be modeled   as finding the maximum distance
 between the energy curve EQ~(\ref{eq:enorm}) and the  performance
 curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors.  This
 represents the minimum energy consumption with minimum execution time (maximum 
-performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then our objective
+performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then the objective
 function has the following form:
 \begin{equation}
   \label{eq:max}
@@ -516,7 +516,7 @@ The nodes in a heterogeneous platform have different computing powers, thus whil
 passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their 
 computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}). 
 These periods are called idle or slack times. 
-Our algorithm takes into account this problem and tries to reduce these slack times when selecting the 
+The algorithm takes into account this problem and tries to reduce these slack times when selecting the 
 frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase 
 the execution times of fast nodes and  minimize the  differences between  the  computation times of 
 fast and slow nodes. The value of the initial frequency scaling factor  for each node is inversely 
@@ -613,7 +613,7 @@ which results in bigger energy savings.
     \EndWhile
     \State  Return $Sopt_1,Sopt_2,\dots,Sopt_N$
   \end{algorithmic}
-  \caption{Heterogeneous scaling algorithm}
+  \caption{frequency scaling factors selection algorithm}
   \label{HSA}
 \end{algorithm}
 
@@ -638,7 +638,7 @@ which results in bigger energy savings.
 \end{algorithm}
 
 \subsection{The verifications of the proposed algorithm}
-\label{sec.verif}
+\label{sec.verif.algo}
 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 
@@ -1055,18 +1055,14 @@ results in less energy saving but less performance degradation.
 
 \section{Conclusion}
 \label{sec.concl} 
-In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors for a heterogeneous platform. 
-This vector gives the maximum distance (optimal tradeoff) between the predicted energy and 
-the predicted performance curves. In addition, we developed a new energy model for measuring  
+In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and 
+the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring  
 and predicting the energy of distributed iterative applications running over heterogeneous 
-cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
-platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
-the proposed algorithm to changes its behaviour to selects different scaling factors  when 
-the number of computing nodes and both of the static and the dynamic powers are changed. 
-
-In the future, we plan to improve this method to apply on asynchronous  iterative applications 
-where each task does not wait the others tasks to finish there works. This leads us to develop a new 
-energy model to an asynchronous iterative applications, where the number of iterations is not 
+platform. To evaluate the proposed method, it  was  applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by  Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also  selects different scaling factors   according to the percentage of the computing and communication times, and according to the values of  the static and  dynamic powers of the CPUs. 
+
+In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, We would like  to develop a similar method that is adapted to asynchronous  iterative applications 
+where each task does not wait for others tasks to finish there works. The development of such method might require a new 
+energy model because the number of iterations is not 
 known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}