X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/6b321df326acc4bb4e33b94f8a963c553fbea89a..bedbe9b0c89738e620fe48b6774039d1e1151de8:/Heter_paper.tex?ds=inline diff --git a/Heter_paper.tex b/Heter_paper.tex index ec89460..78ca998 100644 --- 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 -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 -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 @@ -132,14 +130,14 @@ Section~\ref{sec.optim} details the proposed frequency selecting algorithm then Section~\ref{sec.expe} presents the results of applying the algorithm on the NAS parallel benchmarks and executing them on a heterogeneous platform. It also shows the results of running three different power scenarios and comparing them. -Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works. +Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works. \section{Related works} \label{sec.relwork} 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: @@ -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. -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} -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 -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 -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} @@ -196,7 +192,7 @@ In contrast to the above described papers, this paper presents the following con In this paper, we are interested in reducing the energy consumption of message passing distributed iterative synchronous applications running over -heterogeneous platforms. We define a heterogeneous platform as a collection of +heterogeneous platforms. A heterogeneous platform is defined as a collection of heterogeneous computing nodes interconnected via a high speed homogeneous network. Therefore, each node has different characteristics such as computing power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all @@ -213,7 +209,7 @@ task which have the highest computation time and no slack time. \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} @@ -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} -\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 @@ -282,13 +278,13 @@ to compute the best scaling factors vector. The algorithm complexity is $O(F\cd of iterations and $N$ is the number of computing nodes. The algorithm needs from 12 to 20 iterations to select the best vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}). slowest node, it means only the communication time without any slack time. -Therefore, we can consider the execution time of the iterative application is +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. -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}. -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. @@ -330,8 +326,8 @@ voltage with respect to various frequency values in~\cite{Rauber_Analytical.Mode process of the frequency can be expressed by the scaling factor $S$ which is the ratio between the maximum and the new frequency as in EQ(\ref{eq:s}). The CPU governors are power schemes supplied by the operating -system's kernel to lower a core's frequency. we can calculate the new frequency -$F_{new}$ from EQ(\ref{eq:s}) as follow: +system's kernel to lower a core's frequency. The new frequency +$F_{new}$ from EQ(\ref{eq:s}) can be calculated as follows: \begin{equation} \label{eq:fnew} F_\textit{new} = S^{-1} \cdot F_\textit{max} @@ -357,7 +353,7 @@ and is given by the following equation: \end{equation} The static power is related to the power leakage of the CPU and is consumed during computation and even when idle. As in~\cite{Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling}, -we assume that the static power of a processor is constant + the static power of a processor is considered as constant during idle and computation periods, and for all its available frequencies. The static energy is the static power multiplied by the execution time of the program. According to the execution time model in EQ(\ref{eq:perf}), the execution time of the program @@ -392,8 +388,8 @@ for each processor. It is computed as follows: Reducing the frequencies of the processors according to the vector of scaling factors $(S_1, S_2,\dots, S_N)$ may degrade the performance of the application and thus, increase the static energy because the execution time is -increased~\cite{Kim_Leakage.Current.Moore.Law}. We can measure the overall energy consumption for the iterative -application by measuring the energy consumption for one iteration as in EQ(\ref{eq:energy}) +increased~\cite{Kim_Leakage.Current.Moore.Law}. The overall energy consumption for the iterative +application can be measured by measuring the energy consumption for one iteration as in EQ(\ref{eq:energy}) multiplied by the number of iterations of that application. @@ -422,8 +418,8 @@ The relation between the energy consumption and the execution time for an applic complex and nonlinear, Thus, unlike the relation between the execution time and the scaling factor, the relation of the energy with the frequency scaling factors is nonlinear, for more details refer to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}. -Moreover, they are not measured using the same metric. To solve this problem, we normalize the -execution time by computing the ratio between the new execution time (after +Moreover, they are not measured using the same metric. To solve this problem, the +execution time is normalized by computing the ratio between the new execution time (after scaling down the frequencies of some processors) and the initial one (with maximum frequency for all nodes,) as follows: \begin{multline} @@ -434,7 +430,7 @@ frequency for all nodes,) as follows: \end{multline} -In the same way, we normalize the energy by computing the ratio between the consumed energy +In the same way, the energy is normalized by computing the ratio between the consumed energy while scaling down the frequency and the consumed energy with maximum frequency for all nodes: \begin{multline} \label{eq:enorm} @@ -455,9 +451,9 @@ reduction with minimum execution time reduction. -Our solution for this problem is to make the optimization process for energy and -execution time follow the same direction. Therefore, we inverse the equation of the -normalized execution time which gives the normalized performance equation, as follows: +This problem can be solved by making the optimization process for energy and +execution time follow the same direction. Therefore, the equation of the +normalized execution time is inverted which gives the normalized performance equation, as follows: \begin{multline} \label{eq:pnorm_inv} P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\ @@ -477,11 +473,11 @@ normalized execution time which gives the normalized performance equation, as fo \caption{The energy and performance relation} \end{figure} -Then, we can model our objective function as finding the maximum distance +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 -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} @@ -491,8 +487,8 @@ function has the following form: \overbrace{E_\textit{Norm}(S_{ij})}^{\text{Minimize}} ) \end{equation} where $N$ is the number of nodes and $F$ is the number of available frequencies for each nodes. -Then we can select the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}). -Our objective function can work with any energy model or any power values for each node +Then, the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}) can be selected. +The objective function can work with any energy model or any power values for each node (static and dynamic powers). However, the most energy reduction gain can be achieved when the energy curve has a convex form as shown in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modeling.for.Energy,Hao_Learning.based.DVFS}. @@ -500,7 +496,7 @@ the energy curve has a convex form as shown in~\cite{Zhuo_Energy.efficient.Dynam \label{sec.optim} \subsection{The algorithm details} -In this section we propose algorithm~(\ref{HSA}) which selects the frequency scaling factors +In this section algorithm~(\ref{HSA}) is presented. It selects the frequency scaling factors vector that gives the best trade-off between minimizing the energy consumption and maximizing the performance of a message passing synchronous iterative application executed on a heterogeneous platform. It works online during the execution time of the iterative message passing program. @@ -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. -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 @@ -613,7 +609,7 @@ which results in bigger energy savings. \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} @@ -638,7 +634,7 @@ which results in bigger energy savings. \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 @@ -1055,19 +1051,14 @@ results in less energy saving but less performance degradation. \section{Conclusion} \label{sec.concl} -In this paper, we have presented a new online selecting frequency scaling factors algorithm -that 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 -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}