some corrections

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 38481afebc7ad3838eeca6bee2750f74d9647379..29ff85bd56a5c8548a6d614c1fc8129d07b35cb1 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -138,7 +138,7 @@ Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some f
 DVFS is a technique enabled 
 in modern processors to scale down both the voltage and the frequency of 
 the CPU while computing, in order to reduce the energy consumption of the processor. DVFS is 
 DVFS is a technique enabled 
 in modern processors to scale down both the voltage and the frequency of 
 the CPU while computing, in order to reduce the energy consumption of the processor. DVFS is 

-also  allowed in the GPUs to achieve the same goal. Reducing the frequency of a processor lowers its number of FLOPS and might degrade the performance of the application running on that processor, especially if it is compute bound. Therefore selecting the appropriate frequency for a processor to satisfy some objectives and while taking into account all the constraints, is not a trivial operation.  Many researchers used different strategies to tackle this problem. Some of them developed online methods that compute the new frequency while executing the application, such as ~\cite{Hao_Learning.based.DVFS,Dhiman_Online.Learning.Power.Management}. Others used offline methods that might need to run the application and profile it before selecting the new frequency, such as ~\cite{Rountree_Bounding.energy.consumption.in.MPI,Cochran_Pack_and_Cap_Adaptive_DVFS}. The methods could be heuristics, exact  or brute force methods that satisfy varied objectives such as energy reduction or performance. They also could be adapted to the execution's environment and the type of the application such as sequential, parallel or distributed architecture, homogeneous or heterogeneous platform,  synchronous or asynchronous application, ... 
+also  allowed in the GPUs to achieve the same goal. Reducing the frequency of a processor lowers its number of FLOPS and might degrade the performance of the application running on that processor, especially if it is compute bound. Therefore selecting the appropriate frequency for a processor to satisfy some objectives and while taking into account all the constraints, is not a trivial operation.  Many researchers used different strategies to tackle this problem. Some of them developed online methods that compute the new frequency while executing the application, such as ~\cite{Hao_Learning.based.DVFS,Spiliopoulos_Green.governors.Adaptive.DVFS}. Others used offline methods that might need to run the application and profile it before selecting the new frequency, such as ~\cite{Rountree_Bounding.energy.consumption.in.MPI,Cochran_Pack_and_Cap_Adaptive_DVFS}. The methods could be heuristics, exact  or brute force methods that satisfy varied objectives such as energy reduction or performance. They also could be adapted to the execution's environment and the type of the application such as sequential, parallel or distributed architecture, homogeneous or heterogeneous platform,  synchronous or asynchronous application, ... 

 
 In this paper, we are interested in reducing energy for message passing iterative synchronous applications running over heterogeneous platforms.
 Some works have already been done for such platforms and they can be classified into two types of heterogeneous platforms: 
 
 In this paper, we are interested in reducing energy for message passing iterative synchronous applications running over heterogeneous platforms.
 Some works have already been done for such platforms and they can be classified into two types of heterogeneous platforms: 


@@ -1020,10 +1020,10 @@ results in less energy saving but less performance degradation.
 \begin{figure}
   \centering
   \subfloat[Comparison  of the results on 8 nodes]{%
 \begin{figure}
   \centering
   \subfloat[Comparison  of the results on 8 nodes]{%

-    \includegraphics[width=.30\textwidth]{fig/sen_comp}\label{fig:sen_comp}}%
+    \includegraphics[width=.33\textwidth]{fig/sen_comp}\label{fig:sen_comp}}%

 
   \subfloat[Comparison the selected frequency scaling factors of MG benchmark class C running on 8 nodes]{%
 
   \subfloat[Comparison the selected frequency scaling factors of MG benchmark class C running on 8 nodes]{%

-    \includegraphics[width=.34\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}
+    \includegraphics[width=.33\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}

   \label{fig:comp}
   \caption{The comparison of the three power scenarios}
 \end{figure}  
   \label{fig:comp}
   \caption{The comparison of the three power scenarios}
 \end{figure}  


@@ -1033,18 +1033,20 @@ results in less energy saving but less performance degradation.
 
 \subsection{The comparison of the proposed scaling algorithm }
 \label{sec.compare_EDP}
 
 \subsection{The comparison of the proposed scaling algorithm }
 \label{sec.compare_EDP}

-

 In this section, the scaling  factors selection algorithm
 is compared to Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}. 
 They developed a green governor that regularly applies an online frequency selecting algorithm to reduce the energy consumed by a multicore architecture without degrading much its performance. The algorithm selects the frequencies that minimize the energy and delay products, $EDP=Enegry*Delay$ using the predicted overall energy consumption and execution time delay for each frequency.
 In this section, the scaling  factors selection algorithm
 is compared to Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}. 
 They developed a green governor that regularly applies an online frequency selecting algorithm to reduce the energy consumed by a multicore architecture without degrading much its performance. The algorithm selects the frequencies that minimize the energy and delay products, $EDP=Enegry*Delay$ using the predicted overall energy consumption and execution time delay for each frequency.

- To fairly compare both algorithms, the same energy and execution time models, equations (\ref{eq:energy}) and  (\ref{eq:fnew}), were used for both algorithms to predict the energy consumption and the execution times. Also Spiliopoulos et al.  algorithm was adapted to  start the search from the 
+To fairly compare both algorithms, the same energy and execution time models, equations (\ref{eq:energy}) and  (\ref{eq:fnew}), were used for both algorithms to predict the energy consumption and the execution times. Also Spiliopoulos et al. algorithm was adapted to  start the search from the 

 initial frequencies computed using the equation (\ref{eq:Fint}). The resulting algorithm is an exhaustive search algorithm that minimizes the EDP and has the initial frequencies values as an upper bound.
 
 initial frequencies computed using the equation (\ref{eq:Fint}). The resulting algorithm is an exhaustive search algorithm that minimizes the EDP and has the initial frequencies values as an upper bound.
 

-Both algorithms were applied to the parallel NAS benchmarks to compare their efficiency. Table \ref{table:compare_EDP}  presents the results of comparing the execution times and the energy consumptions for both versions of the NAS benchmarks while running the class C of each benchmark over 8 or 9 heterogeneous nodes. . The results show that our algorithm gives better energy savings than Spiliopoulos et al. algorithm, 
+Both algorithms were applied to the parallel NAS benchmarks to compare their efficiency. Table \ref{table:compare_EDP}  presents the results of comparing the execution times and the energy consumptions for both versions of the NAS benchmarks while running the class C of each benchmark over 8 or 9 heterogeneous nodes. The results show that our algorithm gives better energy savings than Spiliopoulos et al. algorithm, 

 on average it results in 29.76\% energy saving while their algorithm returns just 25.75\%. The average of performance degradation percentage is approximately the same for both algorithms, about 4\%. 
 
 on average it results in 29.76\% energy saving while their algorithm returns just 25.75\%. The average of performance degradation percentage is approximately the same for both algorithms, about 4\%. 
 

+

 For all benchmarks, our algorithm outperforms 
 For all benchmarks, our algorithm outperforms 

-Spiliopoulos et al. algorithm in term of energy and performance tradeoff, see figure (\ref{fig:compare_EDP}) because it maximizes the distance between the energy saving and the performance degradation values while giving the same weight for both metrics. 
+Spiliopoulos et al. algorithm in term of energy and performance tradeoff, see figure (\ref{fig:compare_EDP}), because it maximizes the distance between the energy saving and the performance degradation values while giving the same weight for both metrics. 
+
+

 
 
 \begin{table}[h]
 
 
 \begin{table}[h]


@@ -1068,6 +1070,8 @@ Spiliopoulos et al. algorithm in term of energy and performance tradeoff, see fi
 
 
 
 
 
 

+
+

 \begin{figure}[t]
   \centering
    \includegraphics[scale=0.5]{fig/compare_EDP.pdf}
 \begin{figure}[t]
   \centering
    \includegraphics[scale=0.5]{fig/compare_EDP.pdf}