corrected some errors

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 90793bf0c200e42d0e8a0d6859cb926c3cf68c55..e37939faffe596ff34eb300e8f6d42e7c7d39655 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} 
 and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS},  a heterogeneous cluster composed of two  types 
 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? }
+of Intel and AMD processors. The consumed energy  and the performance  were measured for each frequency, then a linear regression method is used to select the gear that gave the best tradeoff between energy consumption and 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}


@@ -260,7 +256,7 @@ where $TcpOld_i$ is the computation time  of processor $i$ during the first
 iteration and $MinTcm$ is the communication time of the slowest processor from 
 the first iteration.  The model computes the maximum computation time 
 with scaling factor from each node  added to the communication time of the \subsection{The verifications of the proposed method}
 iteration and $MinTcm$ is the communication time of the slowest processor from 
 the first iteration.  The model computes the maximum computation time 
 with scaling factor from each node  added to the communication time of the \subsection{The verifications of the proposed method}

-\label{sec.verif}
+\label{sec.verif.method}

 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 
 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 


@@ -286,9 +282,9 @@ Therefore, the execution time of the iterative application is
 equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied 
 by the number of iterations of that application.
 
 equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied 
 by the number of iterations of that application.
 

-This prediction model is developed from our model for predicting the execution time of 
+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}. 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 

-The execution time prediction model is used in our 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.
 


@@ -481,7 +477,7 @@ Then, the objective function can be modeled   as finding the maximum distance
 between the energy curve EQ~(\ref{eq:enorm}) and the  performance
 curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors.  This
 represents the minimum energy consumption with minimum execution time (maximum 
 between the energy curve EQ~(\ref{eq:enorm}) and the  performance
 curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors.  This
 represents the minimum energy consumption with minimum execution time (maximum 

-performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then our objective
+performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then the objective

 function has the following form:
 \begin{equation}
   \label{eq:max}
 function has the following form:
 \begin{equation}
   \label{eq:max}


@@ -516,7 +512,7 @@ The nodes in a heterogeneous platform have different computing powers, thus whil
 passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their 
 computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}). 
 These periods are called idle or slack times. 
 passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their 
 computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}). 
 These periods are called idle or slack times. 

-Our algorithm takes into account this problem and tries to reduce these slack times when selecting the 
+The algorithm takes into account this problem and tries to reduce these slack times when selecting the 

 frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase 
 the execution times of fast nodes and  minimize the  differences between  the  computation times of 
 fast and slow nodes. The value of the initial frequency scaling factor  for each node is inversely 
 frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase 
 the execution times of fast nodes and  minimize the  differences between  the  computation times of 
 fast and slow nodes. The value of the initial frequency scaling factor  for each node is inversely 


@@ -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}
 


@@ -638,7 +634,7 @@ which results in bigger energy savings.
 \end{algorithm}
 
 \subsection{The verifications of the proposed algorithm}
 \end{algorithm}
 
 \subsection{The verifications of the proposed algorithm}

-\label{sec.verif}
+\label{sec.verif.algo}

 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 
 The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
 EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
 The energy model is also significantly dependent  on the execution time model because the static energy is 


@@ -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}