X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/e25948dd52ceacdc4dc6175e899ae810ea4cc134..fb261c12d6ff09c93a38f3185cedc05431bf7d8c:/Heter_paper.tex?ds=inline

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 90793bf..077de95 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 
@@ -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 
