correcting the last paragraph

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 09061339d3811524f420617c4b8318cb95b838cb..d6ff46fa0ce81414674294b84b953daecbe30cbf 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -183,7 +183,7 @@ used in the method to optimize both the energy consumption and the performance
 of iterative methods, which is presented in the following sections.
 
 
 of iterative methods, which is presented in the following sections.
 
 

-\subsection{Energy model for heterogeneous platform}
+\subsection{Energy model for heterogeneous grid platform}

 
 Many researchers~\cite{Malkowski_energy.efficient.high.performance.computing,
   Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling,
 
 Many researchers~\cite{Malkowski_energy.efficient.high.performance.computing,
   Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling,


@@ -831,7 +831,8 @@ communication ratio. Moreover, as shown in the figure \ref{fig:time_sen}, the ex
 are less by approximately double, linear speed-up, for most of the benchmarks comparing to the one site with 16 nodes scenario. 
 This leads to increased the number of the critical nodes which any one of them may increased the overall the execution time of the benchmarks.
 The EP benchmarks is gives the bigger performance degradation ratio, because there is no 
 are less by approximately double, linear speed-up, for most of the benchmarks comparing to the one site with 16 nodes scenario. 
 This leads to increased the number of the critical nodes which any one of them may increased the overall the execution time of the benchmarks.
 The EP benchmarks is gives the bigger performance degradation ratio, because there is no 

-communications and no slack times in this benchmarks which their performance govern 
+communications and no slack times in this benchmarks which their performance controlled by 
+the computing powers of the nodes.

 The tradeoff between these scenarios can be computed as in the tradeoff function \ref{eq:max}.
 Figure \ref{fig:dist}, presents the tradeoff distance for all benchmarks  over all 
 platform scenarios.  The one site scenario with 16 and 32 nodes had the best tradeoff distance 
 The tradeoff between these scenarios can be computed as in the tradeoff function \ref{eq:max}.
 Figure \ref{fig:dist}, presents the tradeoff distance for all benchmarks  over all 
 platform scenarios.  The one site scenario with 16 and 32 nodes had the best tradeoff distance