X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/ea7e2c38f6a9d8dfeb595068654e8f7432c47f61..c6462f930984f1d5cd3a4674e0ad6dc16ad47c40:/paper.tex

diff --git a/paper.tex b/paper.tex
index 48f38b1..a8629c2 100644
--- a/paper.tex
+++ b/paper.tex
@@ -181,7 +181,7 @@ using a multimeter, the slack times, \dots{} Then a method will exploit these
 measurements to compute the scaling factor values for each processor.  This
 operation, measurements and computing new scaling factor, can be repeated as
 much as needed if the iterations are not regular.  Kimura, Peraza, Yu-Liang et
-al.~\cite{11,2,31} used learning methods to select the appropriate scaling
+al.~\cite{11,2,31} used varied heuristics to select the appropriate scaling
 factor values to eliminate the slack times during runtime.  However, as seen
 in~\cite{39,19}, machine learning methods can take a lot of time to converge
 when the number of available gears is big.  To reduce the impact of slack times,
@@ -208,7 +208,7 @@ selection method which has the following characteristics:
 \item It is well adapted to distributed architectures because it takes into
   account the communication time.
 \item It is well adapted to distributed applications with imbalanced tasks.
-\item it has very small footprint when compared to other methods
+\item It has very small footprint when compared to other methods
   (e.g.,~\cite{19}) and does not require profiling or training as
   in~\cite{38,34}.
 \end{enumerate}
