X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/453664b6bc4c3d4cfee00082a2f26ada681f08a5..36c95dc5298ac5cb8eaaae777eb92ac82c77d7cb:/mpi-energy2-extension/Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index ccccc7f..6754e6f 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -107,7 +107,7 @@
 
 
 
-\title{\AG[]{Optimizing Energy Consumption with DVFS\dots}Energy Consumption Reduction with DVFS for Message \\
+\title{Optimizing Energy Consumption with DVFS for Message \\
          Passing Iterative Applications on \\
                     Grid Architectures} 
   
@@ -490,9 +490,9 @@ In the considered heterogeneous grid platform, each node $j$ in cluster $i$ may
 different dynamic and static powers from the nodes of the other clusters, 
 noted as $\Pd[ij]$ and $\Ps[ij]$ respectively.  Therefore, even if the distributed 
 message passing iterative application is load balanced, the computation time of each CPU $j$ 
-in cluster $i$ noted $\Tcp[ij]$ may be different and different frequency scaling factors may be
+in cluster $i$ noted $\Tcp[ij]$ may be slightly different due to the delay caused by the scheduler of the operating system. Therefore, different frequency scaling factors may be
 computed in order to decrease the overall energy consumption of the application
-and reduce slack times.  The communication time of a processor $j$ in cluster $i$ is noted as
+and reduce the slack times.  The communication time of a processor $j$ in cluster $i$ is noted as
 $\Tcm[ij]$ and could contain slack times when communicating with slower nodes,
 see Figure~\ref{fig:heter}.  Therefore, all nodes do not have equal
 communication times. While the dynamic energy is computed according to the
@@ -772,9 +772,7 @@ Therefore, the algorithm iterates on all remaining frequencies, from the higher
 bound until all nodes reach their minimum frequencies or their lower bounds, to compute the overall
 energy consumption and performance and selects the optimal vector of the frequency scaling
 factors. At each iteration the algorithm determines the slowest node
-according to Equation~\ref{eq:perf}
-%\AG[]{Be consistent: remove word ``Equation'' and add parentheses around equation number, here and all along the rest of the text.}
-and keeps its frequency unchanged,
+according to Equation~\ref{eq:perf} and keeps its frequency unchanged,
 while it lowers the frequency of all other nodes by one gear.  The new overall
 energy consumption and execution time are computed according to the new scaling
 factors.  The optimal set of frequency scaling factors is the set that gives the
@@ -846,8 +844,6 @@ selected clusters and are presented in Table~\ref{table:grid5000}.
 \begin{figure}[!t]
   \centering
   \includegraphics[scale=0.6]{fig/power_consumption.pdf}
-  \AG{I don't understand the labels on the horizontal axis: 10:30:37, 10:30:38,
-    etc.}
   \caption{The power consumption by one core from the Taurus cluster}
   \label{fig:power_cons}
 \end{figure}
