From fb261c12d6ff09c93a38f3185cedc05431bf7d8c Mon Sep 17 00:00:00 2001 From: jean-claude Date: Mon, 24 Nov 2014 14:10:38 +0100 Subject: [PATCH] deleted some our --- Heter_paper.tex | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Heter_paper.tex b/Heter_paper.tex index 5b6e349..077de95 100644 --- a/Heter_paper.tex +++ b/Heter_paper.tex @@ -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 -- 2.39.5