X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/ce79ce10130fe42f774d3898afeb5699b82274b8..1363b18681b25125f146fac02ae6061f10f15606:/paper.tex

diff --git a/paper.tex b/paper.tex
index 657bea7..53a1cf7 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,
@@ -574,9 +574,9 @@ In our cluster there are 18 available frequency states for each processor.  This
 leads to 18 run states for each program.  We use seven MPI programs of the NAS
 parallel benchmarks: CG, MG, EP, FT, BT, LU and SP.  Figure~(\ref{fig:pred})
 presents plots of the real execution times and the simulated ones.  The maximum
-normalized error between these two execution times varies between
-\np{0.0073}\AG[]{unit?} to \np{0.031} dependent on the executed benchmark.  The
-smallest prediction error was for CG and the worst one was for LU.
+normalized error between these two execution times varies between \np{0.0073} to
+\np{0.031} dependent on the executed benchmark.  The smallest prediction error
+was for CG and the worst one was for LU.
 
 \subsection{The experimental results for the scaling algorithm }