correction

[ThesisAhmed.git] / CHAPITRE_04.tex

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index 66a6f0ded596dbb173a74a01c70e02a6540311a8..d9aaf56b813a117d85e6c6a01b68c85eb1c9efb0 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -36,7 +36,7 @@ Section~\ref{ch4:5} details the proposed frequency selecting algorithm.
 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
 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.

 
 
 
 
 
 


@@ -420,11 +420,11 @@ of this application is not directly related to  the number of computing nodes.
   \label{fig:eng_time_dvfs}
 \end{figure}
  Figure  \ref{fig:eng_time_dvfs} (a) shows that the energy 
   \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}
  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.
  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.


@@ -470,7 +470,7 @@ degradation percentages which means that the new execution time of a given versi
  $16.9\%$. 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.
  
  
  $16.9\%$. 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.
  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.


@@ -716,14 +716,14 @@ Size                 & Method                      &\begin{tabular}[c]{@{}l@{}}E
 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. 
 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 
  (\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. 
 
 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. 
 


@@ -734,7 +734,7 @@ As a result, the increase in the heterogeneity between the clusters' computing n
  
 \subsection{Comparing the HSA algorithm to the energy and delay product method}
 \label{res-comp}
  
 \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.
 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.


@@ -759,9 +759,11 @@ Async MS with Async DVFS & 10.32            & -17.04         & 27.36    \\ \hlin
   \caption{Comparing the trade-off percentages of HSA and EDP methods over the Grid'5000}
   \label{fig:compare} 
 \end{figure}
   \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}
 \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. 
 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. 


@@ -770,7 +772,7 @@ energy and performance models  to predict the energy consumption and the executi
 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 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.
 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.