with minimum performance reduction. Many researchers used different strategies
to solve this nonlinear problem for example see~\cite{19,42}, their methods add
big overhead to the algorithm for selecting the suitable frequency. In this
-paper we are present a method to find the optimal scaling factor \emph S for
+paper we present a method to find the optimal scaling factor \emph S for
optimize both energy and performance simultaneously without adding big
overheads. Our solution for this problem is to make the optimization process
have the same direction. Therefore, we inverse the equation of normalize
\subfloat[Real Relation.]{%
\includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
\label{fig:rel}
- \caption{The Energy and Performance Relation}
+ \caption{The Relation of Energy and Performance }
\end{figure*}
Then, we can modelize our objective function as finding the maximum distance
between the energy curve EQ~(\ref{eq:enorm}) and the inverse of performance
\caption{DVFS}
\label{dvfs}
\begin{algorithmic}[1]
- \For {$J:=1$ to $Some-Iterations \; $}
+ \For {$J=1$ to $Some-Iterations \; $}
\State -Computations Section.
\State -Communications Section.
\If {$(J=1)$}
\State -Gather all times of computation and\par\hspace{13 pt} communication from each node.
\State -Call EPSA with these times.
\State -Calculate the new frequency from optimal scale.
- \State -Set the new frequency to the system.
+ \State -Change the new frequency of the system.
\EndIf
\EndFor
\end{algorithmic}
time while it has a big number of available frequencies. The simulated network link is 1 GB Ethernet (TCP/IP).
The backbone of the cluster simulates a high performance switch.
\begin{table}[htb]
- \caption{Platform File Parameters}
+ \caption{SimGrid Platform File Parameters}
% title of Table
\centering
\begin{tabular}{|*{7}{l|}}
NAS parallel benchmarks: CG, MG, EP, FT, BT, LU
and SP. The average normalized errors between the predicted execution time and
the real time (SimGrid time) for all programs is between 0.0032 to 0.0133. AS an
-example, we are present the execution times of the NAS benchmarks as in the
+example, we present the execution times of the NAS benchmarks as in the
figure~(\ref{fig:pred}).
\subsection{The EPSA Results}
\includegraphics[width=.33\textwidth]{lu.eps}\hfill%
\includegraphics[width=.33\textwidth]{bt.eps}\hfill%
\includegraphics[width=.33\textwidth]{ft.eps}
- \caption{Optimal scaling factors for The NAS MPI Programs}
+ \caption{The Discovered scaling factors for NAS MPI Programs}
\label{fig:nas}
\end{figure*}
\begin{table}[htb]
\end{table}
As shown in these tables our scaling factor is not optimal for energy saving
such as Rauber and Rünger scaling factor EQ~(\ref{eq:sopt}), but it is optimal for both
-the energy and the performance simultaneously. Our $EPSA$ optimal scaling factors
+the energy and the performance simultaneously. Our EPSA optimal scaling factors
has better simultaneous optimization for both the energy and the performance
compared to Rauber and Rünger energy-performance method ($R_{E-P}$). Also, in
($R_{E-P}$) method when setting the frequency to maximum value for the
\includegraphics[width=.33\textwidth]{compare_class_A.pdf}
\includegraphics[width=.33\textwidth]{compare_class_B.pdf}
\includegraphics[width=.33\textwidth]{compare_class_c.pdf}
- \caption{Comparing Our EPSA with Rauber and Rünger Methods}
+ \caption {Comparing Our EPSA with Rauber and Rünger Methods}
\label{fig:compare}
\end{figure}
\section{Conclusion}
\label{sec.concl}
-In this paper we develop the simultaneous energy-performance algorithm. It works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value refer to more energy saving. Also, when the scaling factor is smaller value, Then it has bigger impact on performance than energy. The algorithm optimizes the energy saving and performance in the same time to have positive trade off. The optimal trade off represents the maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. In future, we will apply the EPSA algorithm on heterogeneous platform.
+In this paper we developed the simultaneous energy-performance algorithm. It works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value refer to more energy saving. Also, when the scaling factor is smaller value, Then it has bigger impact on performance than energy. The algorithm optimizes the energy saving and performance in the same time to have positive trade off. The optimal trade off represents the maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. In future, we will apply the EPSA algorithm on heterogeneous platform.
\section*{Acknowledgment}
Computations have been performed on the supercomputer facilities of the
