University of Franche-Comté\\
IUT de Belfort-Montbéliard, 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
Fax : +33~3~84~58~77~32\\
- Email: \{jean-claude.charr, ahmed.fanfakh\_badri\_muslim, raphael.couturier, arnaud.giersch\}@univ-fcomte.fr
+ Email: \{jean-claude.charr,raphael.couturier,ahmed.fanfakh\_badri\_muslim,arnaud.giersch\}@univ-fcomte.fr
}
}
\section{Related Works}
\label{sec.relwork}
-\AG{Consider introducing the models sec.~\ref{sec.exe} maybe before related works}
+\AG{Consider introducing the models (sec.~\ref{sec.exe}) before related works}
In this section, some heuristics to compute the scaling factor are
presented and classified into two categories: offline and online methods.
refer to this scenario as $R_{E}$. The second scenario is similar to the first
except setting the slower task to the maximum frequency (when the scale $S=1$)
to keep the performance from degradation as mush as possible. We refer to this
-scenario as $R_{E-P}$. The comparison is made in tables~(\ref{table:compare
- Class A},\ref{table:compare Class B},\ref{table:compare Class C}). These
+scenario as $R_{E-P}$. The comparison is made in tables~(\ref{table:compareA},
+\ref{table:compareB}, and \ref{table:compareC}). These
tables show the results of our method and Rauber and Rünger scenarios for all the
NAS benchmarks programs for classes A,B and C.
\begin{table}[p]
As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
Babylon (Iraq) for supporting his work.
-\JC{delete the online paths for each reference}
+\JC{delete the online paths for each reference\AG{except for TOP500 and the NPB}}
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