From: jccharr <jccharr@charr.local>
Date: Tue, 27 May 2014 09:51:19 +0000 (+0200)
Subject: Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/mpi-energy
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/commitdiff_plain/6df121309c573dd7f9227e2cc22d0075c94aac65?ds=inline;hp=-c

Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/mpi-energy
ok
---

6df121309c573dd7f9227e2cc22d0075c94aac65
diff --combined paper.tex
index 5ad7af7,da3fb81..b4dc51c
--- a/paper.tex
+++ b/paper.tex
@@@ -136,7 -136,7 +136,7 @@@ the MPI program to choose the scaling f
  predict both energy consumption and execution time over all available scaling
  factors.  The prediction achieved depends on some computing time information,
  gathered at the beginning of the runtime.  We apply this algorithm to the NAS parallel benchmarks (NPB v3.3)~\cite{44}.  Our experiments are executed using the simulator
- SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183} over an homogeneous
+ SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183} over a homogeneous
  distributed memory architecture.  Furthermore, we compare the proposed algorithm
  with Rauber and Rünger methods~\cite{3}.  The comparison's results show that our
  algorithm gives better energy-time trade-off.
@@@ -228,7 -228,7 +228,7 @@@ our paper is to present a new online sc
  %   paper in homogeneous clusters}
  
  
 -\section{Energy model for a homogeneous platform}
 +\section{Energy model for an homogeneous platform}
  \label{sec.exe}
  Many researchers~\cite{9,3,15,26} divide the power consumed by a processor into
  two power metrics: the static and the dynamic power.  While the first one is
@@@ -682,7 -682,7 +682,7 @@@ trade-offs such as in BT and EP
  
  In this paper, we have presented a new online scaling factor selection method
  that optimizes simultaneously the energy and performance of a distributed
- application running on an homogeneous cluster.  It uses the computation and
+ application running on a homogeneous cluster.  It uses the computation and
  communication times measured at the first iteration to predict energy
  consumption and the performance of the parallel application at every available
  frequency.  Then, it selects the scaling factor that gives the best trade-off