Section~\ref{ch4:6} presents the iterative multi-splitting application which is a hybrid method and was used as a benchmark to evaluate the efficiency of the proposed algorithm.
Section~\ref{ch4:7} presents the simulation results of applying the algorithm on the multi-splitting application
and executing it on different grid scenarios. It also shows the results of running
-three different power scenarios and comparing them. Moreover, in the last subsection, the proposed algorithm is compared to the energy and delay product (EDP) method. Section \ref{ch4:8} presenting the results of real experiments executed over the Grid'5000 platform and compared to the EDP method. Finally, the chapter ends with a summary.
+three different power scenarios and comparing them. Moreover, in the last subsection, the proposed algorithm is compared to the energy and delay product (EDP) method. Section \ref{ch4:8} presents the results of real experiments executed over the Grid'5000 platform and compared to the EDP method. Finally, the chapter ends with a summary.
\label{fig:eng_time_dvfs}
\end{figure}
Figure \ref{fig:eng_time_dvfs} (a) shows that the energy
- consumption of all four versions of the method, running over the 8 grid scenarios described in Table \ref{table:comp}, are not affected by the increase in the number of computing nodes. MS without applying DVFS operations had the same behavior. On the other hand, Figure \ref{fig:eng_time_dvfs} (b) shows that the execution time of the MS application with DVFS operations
+ consumption of all four versions of the method, running over the 8 grid scenarios described in Table \ref{table:comp}, are not affected by the increase in the number of computing nodes. MS without applying DVFS operations had the same behaviour. On the other hand, Figure \ref{fig:eng_time_dvfs} (b) shows that the execution time of the MS application with DVFS operations
decreases in inverse proportion to the number of nodes. Moreover, it can be noticed that the asynchronous MS with synchronous DVFS consumes less energy when compared to the other versions of the method. Two reasons explain this energy consumption reduction:
\begin{enumerate}
- \item The asynchronous MS with synchronous DVFS version uses synchronous DVFS communications which allow it to apply the new computed frequencies at the begining of the second iteration. Thus, reducing the consumption of dynamic energy by the application from the second iteration until the end of the application. Whereas in
- asynchronous DVFS versions where the DVFS communications are asynchronous, the new frequencies cannot be computed at the end of the first iteration and consequently cannot be applied at the begining of the second iteration.
+ \item The asynchronous MS with synchronous DVFS version uses synchronous DVFS communications which allow it to apply the new computed frequencies at the beginning of the second iteration. Thus, reducing the consumption of dynamic energy by the application from the second iteration until the end of the application. Whereas in
+ asynchronous DVFS versions where the DVFS communications are asynchronous, the new frequencies cannot be computed at the end of the first iteration and consequently cannot be applied at the beginning of the second iteration.
Indeed, since the performance information gathered during the first iteration is not sent synchronously at the end of the first iteration, fast nodes might execute many iterations before receiving the performance information, computing the new frequencies based on this information and applying the new computed frequencies. Therefore, many iterations might be computed by CPUs running on their highest frequency and consuming more dynamic energy than scaled down processors.
\item As shown in Figure \ref{fig:eng_time_ms} (b), the execution time of the asynchronous MS version is lower than the execution time of the synchronous MS version because there is no idle time in the asynchronous version and the communications are overlapped by computations. Since the consumption of static energy is proportional to the execution time, the asynchronous MS version consumes less static energy than the synchronous version.
$16.9\%$. While the worst case is the synchronous MS with synchronous DVFS where the performance is on average degraded by $2.9\%$ when compared to the reference method.
- The energy consumption and performance tradeoff between these five versions is presented in Figure \ref{fig:dist}.
+ The energy consumption and performance trade-off between these five versions is presented in Figure \ref{fig:dist}.
These distance values are computed as the differences between the energy saving
and the performance degradation percentages as in the optimization function
(\ref{eq:max-grid}). Thus, the best MS version is the one that has the maximum distance between the energy saving and performance degradation. The distance can be negative if the energy saving percentage is less than the performance degradation percentage.
Table \ref{table:exper} shows that there are positive and negative performance
degradation percentages. A negative value means that the new execution time of a given version of the application is less than the execution time of the synchronous MS without DVFS.
Therefore, the version with the smallest negative performance degradation percentage has actually the best speed up when compared to the other versions.
- The energy consumption and performance tradeoffs between these four versions can be computed as in the optimization Function
+ The energy consumption and performance trade-offs between these four versions can be computed as in the optimization Function
(\ref{eq:max-grid}). The asynchronous MS applying synchronously the HSA algorithm gives the best distance which is equal to $48.41\%$.
This version saves up to $26.93\%$ of energy and even reduces the execution time of the application by
$21.48\%$. This overall improvement is due to combining asynchronous computing and the synchronous application of the HSA algorithm.
Finally, this section shows that the obtained results over Grid'5000 are comparable to the
-simulation results of section \ref{ch4:7:2}, the asynchronous MS applying synchronously the HSA algorithm is the best version in both of sections. Moreover, the results over Grid'5000 are better
+simulation results of Section \ref{ch4:7:2}, the asynchronous MS applying synchronously the HSA algorithm is the best version in both of sections. Moreover, the results over Grid'5000 are better
than simulation results because the computing clusters used in the Grid'5000 experiments are more heterogeneous in term of the computing power and network characteristics than the simulated platform with SimGrid. For example, the nodes in StRemi cluster have lower computing powers compared to the other used three clusters of Grid'5000 platform.
As a result, the increase in the heterogeneity between the clusters' computing nodes increases the idle times which forces the proposed algorithm to select a big scaling factors and thus saving more energy.
\subsection{Comparing the HSA algorithm to the energy and delay product method}
\label{res-comp}
-The EDP algorithm, described in section \ref{ch4:7:5}, was applied synchronously and asynchronously to both the synchronous and asynchronous MS application of size $N=400^3$. The experiments were conducted over 4 distributed clusters, described in Table \ref{table:grid5000}, and 8 homogeneous nodes were used from each cluster.
+The EDP algorithm, described in Section \ref{ch4:7:5}, was applied synchronously and asynchronously to both the synchronous and asynchronous MS application of size $N=400^3$. The experiments were conducted over 4 distributed clusters, described in Table \ref{table:grid5000}, and 8 homogeneous nodes were used from each cluster.
Table \ref{table:comapre} presents the results of energy saving, performance degradation and distance percentages when applying the EDP method on four different MS versions.
Figure \ref{fig:compare} compares the distance percentages, computed as the difference between energy saving and performance degradation percentages, of the EDP and HSA
algorithms. This comparison shows that the proposed HSA algorithm gives better energy reduction and performance trade-off than the EDP method. EDP gives better results when evaluated over Grid'5000 than over the simulator because the nodes used from Grid'5000 are more heterogeneous than those simulated with SimGrid.
\caption{Comparing the trade-off percentages of HSA and EDP methods over the Grid'5000}
\label{fig:compare}
\end{figure}
+
+
+
\section{Conclusions}
\label{ch4:9}
-
This chapter presents a new online frequency selection algorithm for asynchronous iterative
applications running over a grid. It selects the best vector of frequencies that maximizes
the distance between the predicted energy consumption and the predicted execution time.
The proposed algorithm was evaluated twice over the SimGrid simulator and Grid'5000 testbed while running a multi-splitting (MS) application that solves 3D problems.
The experiments were executed over different
grid scenarios composed of different numbers of clusters and different numbers of nodes per cluster.
- The HSA algorithm was applied synchronously and asynchronously on a synchronous and an asynchronous version of the MS application. Both the simulation and real experiment results show that applying synchronous HSA algorithm on an asynchronous MS application gives the best tradeoff between energy consumption reduction and performance compared to other scenarios.
+ The HSA algorithm was applied synchronously and asynchronously on a synchronous and an asynchronous version of the MS application. Both the simulation and real experiment results show that applying synchronous HSA algorithm on an asynchronous MS application gives the best trade-off between energy consumption reduction and performance compared to other scenarios.
In the simulation results, this scenario saves on average the energy consumption by 22\% and reduces the execution time of the application by 5.72\%. This version optimizes both of the dynamic energy consumption by applying synchronously the HSA algorithm at the end of the first iteration and the static energy consumption by using asynchronous communications between nodes from different clusters which are overlapped by computations. The HSA algorithm was also evaluated over three power scenarios. As expected, the algorithm selects different vectors of frequencies for each power scenario. The highest energy consumption reduction was achieved in the power scenario with the highest dynamic power and the lowest performance degradation was obtained in the power scenario with the highest static power.
The proposed algorithm was compared to another method that
uses the well known energy and delay product as an objective function.
