some corrections

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 52883a348ee456aad94890c516f64eef1dcf7289..29ff85bd56a5c8548a6d614c1fc8129d07b35cb1 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -89,9 +89,7 @@ for each node computing the message passing iterative application. The algorithm
 works without training or profiling. It uses a new energy model for message passing iterative applications 
 running on a heterogeneous platform. The proposed algorithm is evaluated  on the Simgrid simulator while 
 running the NAS parallel benchmarks. The experiments demonstrated that it reduces the energy consumption 
 works without training or profiling. It uses a new energy model for message passing iterative applications 
 running on a heterogeneous platform. The proposed algorithm is evaluated  on the Simgrid simulator while 
 running the NAS parallel benchmarks. The experiments demonstrated that it reduces the energy consumption 

-up to 35\% while limiting the performance degradation as much as possible. \textcolor{red}{Furthermore, we compare the
-proposed algorithm with other method. The comparison results show that our algorithm gives better
-energy-time trade-off.}
+up to 35\% while limiting the performance degradation as much as possible. Finally, the algorithm is compared to an existing method and the comparison results show that it outperforms the latter.

 
 \end{abstract}
 
 
 \end{abstract}
 


@@ -131,8 +129,8 @@ consumption while minimizing the degradation of the program's performance.
 Section~\ref{sec.optim} details the proposed frequency selecting algorithm then the precision of the proposed algorithm is verified. 
 Section~\ref{sec.expe} presents the results of applying the algorithm on  the NAS parallel benchmarks and executing them 
 on a heterogeneous platform. It shows the results of running three 
 Section~\ref{sec.optim} details the proposed frequency selecting algorithm then the precision of the proposed algorithm is verified. 
 Section~\ref{sec.expe} presents the results of applying the algorithm on  the NAS parallel benchmarks and executing them 
 on a heterogeneous platform. It shows the results of running three 

-different power scenarios and comparing them. \textcolor{red}{Moreover, it also shows the comparison results
-between our method and other method.}
+different power scenarios and comparing them. Moreover, it also shows the comparison results
+between the proposed method and an existing method.

 Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.
 
 \section{Related works}
 Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.
 
 \section{Related works}


@@ -140,7 +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: 


@@ -270,7 +268,7 @@ by the number of iterations of that application.
 
 This prediction model is developed from the model for predicting the execution time of 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 
 
 This prediction model is developed from the model for predicting the execution time of 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 

-The execution time prediction model is uSpiliopoulossed in the method for optimizing both 
+The execution time prediction model is used in the method for optimizing both 

 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 


@@ -1022,10 +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}  


@@ -1035,27 +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.
+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.
+
+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\%. 
+
+
+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. 
+

 
 

-In this section, we compare our scaling  factors selection algorithm
-with Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}. 
-They developed an online frequency selecting algorithm running over multicore architecture. 
-The algorithm predicted both the energy and performance during the runtime of the program, then 
-selecting the frequencies that minimized the energy and delay products (EDP), $EDP=Enegry*Delay$. 
-To be able to compare with this algorithm, we used our energy and execution time models in prediction process,
-equations (\ref{eq:energy}) and  (\ref{eq:fnew}). Also their algorithm is adapted to taking into account 
-the heterogeneous platform to starts selecting the 
-initial frequencies using the equation (\ref{eq:Fint}). The algorithm built to test all possible frequencies as 
-a brute-force search algorithm. 
-
-The comparison results of running NAS benchmarks class C on 8 or 9 nodes are 
-presented in table \ref{table:compare_EDP}. The results show that our algorithm has a biggest energy saving percentage, 
-on average it has 29.76\% and thier algorithm has 25.75\%,
-while the average of performance degradation percentage is approximately the same, the average for our algorithm is 
-equal to 3.89\% and for their algorithm is equal to 4.03\%. In general, our algorithm outperforms 
-Spiliopoulos et al. algorithm in term of energy and performance tradeoff see figure (\ref{fig:compare_EDP}). 
-This because our algorithm maximized the difference (the distance) between the energy saving and the performance degradation 
-comparing to their EDP optimization function. It is also keeps the frequency of the slowest node without change 
-that gave some enhancements to the energy and performance tradeoff.

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


@@ -1079,6 +1070,8 @@ that gave some enhancements to the energy and performance tradeoff.
 
 
 
 
 
 

+
+

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


@@ -1092,8 +1085,8 @@ that gave some enhancements to the energy and performance tradeoff.
 In this paper, a new online frequency selecting algorithm has been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and 
 the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring  
 and predicting the energy of distributed iterative applications running over heterogeneous 
 In this paper, a new online frequency selecting algorithm has been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and 
 the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring  
 and predicting the energy of distributed iterative applications running over heterogeneous 

-platform. To evaluate the proposed method, it  was  applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by  Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also  selects different scaling factors   according to the percentage of the computing and communication times, and according to the values of  the static and  dynamic powers of the CPUs. \textcolor{red}{ We compare our algorithm with Spiliopoulos et al. algorithm, the comparison results showed that our 
-algorithm outperforms their algorithm in term of energy-time tradeoff.}
+platform. To evaluate the proposed method, it  was  applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by  Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also  selects different scaling factors   according to the percentage of the computing and communication times, and according to the values of  the static and  dynamic powers of the CPUs. Finally, the algorithm was compared to Spiliopoulos et al. algorithm and the results showed that it 
+ outperforms their algorithm in term of energy-time tradeoff.

 
 In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, we would like  to develop a similar method that is adapted to asynchronous  iterative applications 
 where each task does not wait for others tasks to finish there works. The development of such method might require a new 
 
 In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, we would like  to develop a similar method that is adapted to asynchronous  iterative applications 
 where each task does not wait for others tasks to finish there works. The development of such method might require a new