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

ok

diff --git a/paper.tex b/paper.tex
index 5ad7af76ce6da09287cb412595019f6f772c43a1..b4dc51c59eacb810de98d3581105aa6271b3b114 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -136,7 +136,7 @@ the MPI program to choose the scaling factor.  This algorithm has the ability to
 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.
@@ -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