modifications for the related work

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 077de95ef50117837547c2bbe8cbd6a9cb69e35d..78ca9987e439e38604e4c67620b62190d75dbead 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -111,15 +111,13 @@ This heterogeneous platform executes more than four  GFLOPS per watt.
 
 Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, 
 such as scheduling, DVFS, ... DVFS is a widely  used process to reduce the energy consumption of a processor by lowering 
 
 Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, 
 such as scheduling, DVFS, ... DVFS is a widely  used process to reduce the energy consumption of a processor by lowering 

-its frequency \cite{Rizvandi_Some.Observations.on.Optimal.Frequency}. However, it also the reduces the number of FLOPS 
+its frequency \cite{Rizvandi_Some.Observations.on.Optimal.Frequency}. However, it also  reduces the number of FLOPS 

 executed by the processor which might increase  the execution time of the application running over that processor.
 Therefore, researchers used different optimization strategies to select the frequency that gives the best tradeoff  
 between the energy reduction and 
 executed by the processor which might increase  the execution time of the application running over that processor.
 Therefore, researchers used different optimization strategies to select the frequency that gives the best tradeoff  
 between the energy reduction and 

-performance degradation ratio. \textbf{In our previous paper \cite{Our_first_paper},  a frequency selecting algorithm 
-was proposed for distributed iterative application running over homogeneous platform. While in this paper the algorithm is  significantly adapted to run over a heterogeneous platform. This platform is a collection of heterogeneous computing nodes interconnected via a high speed homogeneous network.}
-
-The proposed frequency selecting algorithm selects the vector of frequencies for a heterogeneous platform that runs a message passing iterative application,  that gives the maximum energy reduction and minimum 
-performance degradation ratio simultaneously. The algorithm has a very small 
+performance degradation ratio. In \cite{Our_first_paper},  a frequency selecting algorithm 
+was proposed to reduce the energy consumption of message passing iterative applications running over homogeneous platforms. The  results of the experiments showed significant energy consumption reductions. In this paper,  a new frequency selecting algorithm  adapted for heterogeneous platform  is presented. It selects the vector of frequencies, for a heterogeneous platform running a message passing iterative application,  that simultaneously gives the maximum energy reduction and minimum 
+performance degradation ratio. The algorithm has a very small 

 overhead, works online and does not need any training or profiling.  
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents some
 overhead, works online and does not need any training or profiling.  
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents some


@@ -139,7 +137,7 @@ 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 used online methods that compute the new frequency while executing the application \textbf{add a reference for an online method here}. Others used offline methods that might need to run the application and profile it before selecting the new frequency \textbf{add a reference for an offline method}. 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,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, ... 

 
 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 it 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 it can be classified into two types of heterogeneous platforms: 


@@ -161,19 +159,17 @@ In~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Rong et al. showed that
 a heterogeneous (GPUs and CPUs) cluster that enables DVFS gave better energy and performance 
 efficiency than other clusters only composed of  CPUs.
  
 a heterogeneous (GPUs and CPUs) cluster that enables DVFS gave better energy and performance 
 efficiency than other clusters only composed of  CPUs.
  

-The work presented in this paper concerns the second type of platform,, with heterogeneous CPUs.
+The work presented in this paper concerns the second type of platform, with heterogeneous CPUs.

 Many methods were conceived to reduce the energy consumption of this type of platform.  Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling}  
 Many methods were conceived to reduce the energy consumption of this type of platform.  Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling}  

-developed a method that minimize the value of $energy*delay^2$ by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster. \textbf{should define the delay} Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} propose
+developed a method that minimizes the value of $energy*delay^2$ (the delay is the sum of slack times that happen during synchronous communications) by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster.. Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} propose

 an algorithm that divides the executed tasks into two types: the critical and 
 non critical tasks. The algorithm scales down the frequency of  non critical tasks proportionally to their  slack and communication times while limiting  the performance degradation percentage to less than 10\%. In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS} 
 an algorithm that divides the executed tasks into two types: the critical and 
 non critical tasks. The algorithm scales down the frequency of  non critical tasks proportionally to their  slack and communication times while limiting  the performance degradation percentage to less than 10\%. In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS} 

-and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS},  a heterogeneous cluster composed of two  types 
-of Intel and AMD processors. The consumed energy 
-and the performance for each frequency gear were predicted, then the algorithm selected the best gear that gave 
-the best tradeoff. \textbf{what energy model they used? what method they used? }
+  a heterogeneous cluster composed of two  types 
+of Intel and AMD processors. They use a gradient method to predict the impact of DVFS operations on performance.

 In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, 
  the best frequencies for a specified heterogeneous cluster are selected offline using some 
 In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, 
  the best frequencies for a specified heterogeneous cluster are selected offline using some 

-heuristic. Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic approach to  
-minimize the power consumption of heterogeneous severs  with time/space complexity \textbf{what does it mean}. This approach 
+heuristic. Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic programming approach to  
+minimize the power consumption of heterogeneous severs  while respecting given time constraints. This approach 

 had considerable overhead.
 In contrast to the above described papers, this paper presents the following contributions :
 \begin{enumerate}
 had considerable overhead.
 In contrast to the above described papers, this paper presents the following contributions :
 \begin{enumerate}


@@ -213,7 +209,7 @@ task which have the highest computation time and no slack time.
   
  \begin{figure}[t]
   \centering
   
  \begin{figure}[t]
   \centering

-    \includegraphics[scale=0.6]{fig/commtasks}
+   \includegraphics[scale=0.6]{fig/commtasks}

   \caption{Parallel tasks on a heterogeneous platform}
   \label{fig:heter}
 \end{figure}
   \caption{Parallel tasks on a heterogeneous platform}
   \label{fig:heter}
 \end{figure}


@@ -613,7 +609,7 @@ which results in bigger energy savings.
     \EndWhile
     \State  Return $Sopt_1,Sopt_2,\dots,Sopt_N$
   \end{algorithmic}
     \EndWhile
     \State  Return $Sopt_1,Sopt_2,\dots,Sopt_N$
   \end{algorithmic}

-  \caption{Heterogeneous scaling algorithm}
+  \caption{frequency scaling factors selection algorithm}

   \label{HSA}
 \end{algorithm}
 
   \label{HSA}
 \end{algorithm}
 


@@ -1055,18 +1051,14 @@ results in less energy saving but less performance degradation.
 
 \section{Conclusion}
 \label{sec.concl} 
 
 \section{Conclusion}
 \label{sec.concl} 

-In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors for a heterogeneous platform. 
-This vector gives the maximum distance (optimal tradeoff) between the predicted energy and 
-the predicted performance curves. In addition, we developed a new energy model for measuring  
+In this paper, a new online frequency selecting algorithm have 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 
 and predicting the energy of distributed iterative applications running over heterogeneous 

-cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
-platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
-the proposed algorithm to changes its behaviour to selects different scaling factors  when 
-the number of computing nodes and both of the static and the dynamic powers are changed. 
-
-In the future, we plan to improve this method to apply on asynchronous  iterative applications 
-where each task does not wait the others tasks to finish there works. This leads us to develop a new 
-energy model to an asynchronous iterative applications, where the number of iterations is not 
+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. 
+
+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 
+energy model because the number of iterations is not 

 known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}
 known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}