From 5ef38a410118e911f3dd1a8c5e3cafe44227e013 Mon Sep 17 00:00:00 2001 From: afanfakh Date: Tue, 29 Sep 2015 10:04:20 +0200 Subject: [PATCH] correcting the last paragraph --- mpi-energy2-extension/Heter_paper.tex | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex index 0906133..d6ff46f 100644 --- 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. -\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, @@ -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 -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 -- 2.39.5