X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/a79756ce23315cc6ab9203a3adc8cf6919e746f0..47017a08cb1074a60d85aae0f647d757c576c8b7:/mpi-energy2-extension/Heter_paper.tex?ds=sidebyside diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex index ea21ce7..3fa9968 100644 --- a/mpi-energy2-extension/Heter_paper.tex +++ b/mpi-energy2-extension/Heter_paper.tex @@ -147,10 +147,10 @@ scaling (DVFS) is one of them. It can be used to reduce the power consumption of The algorithm has a small overhead and works without training or profiling. It uses a new energy model for message passing iterative applications running on a grid. - The proposed algorithm is evaluated on a real grid, the grid'5000 platform, while - running the NAS parallel benchmarks. The experiments show that it reduces the - energy consumption on average by \np[\%]{30} while the performance is only degraded - on average by \np[\%]{3.2}. Finally, the algorithm is + The proposed algorithm is evaluated on a real grid, the Grid'5000 platform, while + running the NAS parallel benchmarks. The experiments on 16 nodes, distributed on three clusters, show that it reduces on average the + energy consumption by \np[\%]{30} while the performance is on average only degraded + by \np[\%]{3.2}. Finally, the algorithm is compared to an existing method. The comparison results show that it outperforms the latter in terms of energy consumption reduction and performance. \end{abstract} @@ -188,7 +188,7 @@ the Tianhe-2 platform is approximately more than \$10 million each year. The computing platforms must be more energy efficient and offer the highest number of FLOPS per watt possible, such as the Shoubu-ExaScaler from RIKEN which became the top of the Green500 list in June 2015 \cite{Green500_List}. -This heterogeneous platform executes more than 7 GFLOPS per watt while consuming +This heterogeneous platform executes more than 7 GFlops per watt while consuming 50.32 kilowatts. Besides platform improvements, there are many software and hardware techniques @@ -200,18 +200,22 @@ the number of FLOPS executed by the processor which may increase the execution time of the application running over that processor. Therefore, researchers use different optimization strategies to select the frequency that gives the best trade-off between the energy reduction and performance degradation ratio. In -\cite{Our_first_paper} and \cite{pdsec2015} , a frequencies selecting algorithm was proposed to reduce -the energy consumption of message passing iterative applications running over -homogeneous and heterogeneous clusters respectively. -The results of the experiments showed significant energy -consumption reductions. All the experimental results were conducted over -Simgrid simulator \cite{SimGrid}, which offers easy tools to create a homogeneous and heterogeneous platforms and run message passing parallel applications over them. In this paper, a new frequencies selecting algorithm, -adapted to grid platforms composed of heterogeneous clusters, is presented. It is applied to the NAS parallel benchmarks and evaluated over a real testbed, -the grid'5000 platform \cite{grid5000}. It selects for a grid platform running a message passing iterative -application the vector of -frequencies that simultaneously tries to offer the maximum energy reduction and -minimum performance degradation ratios. The algorithm has a very small overhead, -works online and does not need any training or profiling. +\cite{Our_first_paper} and \cite{pdsec2015}, a frequency selecting algorithm +was proposed to reduce the energy consumption of message passing iterative +applications running over homogeneous and heterogeneous clusters respectively. +The results of the experiments showed significant energy consumption +reductions. All the experimental results were conducted over the SimGrid +simulator \cite{SimGrid}, which offers easy tools to describe homogeneous and heterogeneous platforms, and to simulate the execution of message passing parallel +applications over them. + +In this paper, a new frequency selecting algorithm, adapted to grid platforms +composed of heterogeneous clusters, is presented. It is applied to the NAS +parallel benchmarks and evaluated over a real testbed, the Grid'5000 platform +\cite{grid5000}. It selects for a grid platform running a message passing +iterative application the vector of frequencies that simultaneously tries to +offer the maximum energy reduction and minimum performance degradation +ratios. 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 @@ -223,10 +227,11 @@ energy-performance objective function that maximizes the reduction of energy consumption while minimizing the degradation of the program's performance. Section~\ref{sec.optim} details the proposed frequencies selecting algorithm. Section~\ref{sec.expe} presents the results of applying the algorithm on the -NAS parallel benchmarks and executing them on the grid'5000 testbed. +NAS parallel benchmarks and executing them on the Grid'5000 testbed. It also evaluates the algorithm over multi-cores per node architectures and over three different power scenarios. Moreover, it shows the comparison results between the proposed method and an existing method. Finally, in Section~\ref{sec.concl} the paper ends with a summary and some future works. + \section{Related works} \label{sec.relwork} @@ -266,7 +271,7 @@ heterogeneous cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal was to maximize the energy efficiency of the platform during computation by maximizing the number of FLOPS per watt generated. In~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, Kai Ma et -al. developed a scheduling algorithm that distributes workloads proportional to +al. developed a scheduling algorithm that distributes workload proportional to the computing power of the nodes which could be a GPU or a CPU. All the tasks must be completed at the same time. In~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Rong et al. showed that a heterogeneous (GPUs and CPUs) cluster that enables @@ -305,7 +310,7 @@ following contributions : \item a new online frequency selecting algorithm for heterogeneous grid platforms. The algorithm has a very small overhead and does not need any - training or profiling. It uses a new optimization function which + training nor profiling. It uses a new optimization function which simultaneously maximizes the performance and minimizes the energy consumption of a message passing iterative synchronous application. @@ -325,13 +330,6 @@ heterogeneous grid platforms. A heterogeneous grid platform could be defined as heterogeneous computing clusters interconnected via a long distance network which has lower bandwidth and higher latency than the local networks of the clusters. Each computing cluster in the grid is composed of homogeneous nodes that are connected together via high speed network. Therefore, each cluster has different characteristics such as computing power (FLOPS), energy consumption, CPU's frequency range, network bandwidth and latency. -\begin{figure}[!t] - \centering - \includegraphics[scale=0.6]{fig/commtasks} - \caption{Parallel tasks on a heterogeneous platform} - \label{fig:heter} -\end{figure} - The overall execution time of a distributed iterative synchronous application over a heterogeneous grid consists of the sum of the computation time and the communication time for every iteration on a node. However, due to the @@ -341,6 +339,13 @@ nodes to finish their computations (see Figure~\ref{fig:heter}). Therefore, the overall execution time of the program is the execution time of the slowest task which has the highest computation time and no slack time. +\begin{figure}[!t] + \centering + \includegraphics[scale=0.6]{fig/commtasks} + \caption{Parallel tasks on a heterogeneous platform} + \label{fig:heter} +\end{figure} + Dynamic Voltage and Frequency Scaling (DVFS) is a process, implemented in modern processors, that reduces the energy consumption of a CPU by scaling down its voltage and frequency. Since DVFS lowers the frequency of a CPU @@ -374,18 +379,20 @@ scaling factors, the communication time and the computation time for all the tasks must be measured during the first iteration before applying any DVFS operation. Then the execution time for one iteration of the application with any vector of scaling factors can be predicted using (\ref{eq:perf}). +% \begin{equation} \label{eq:perf} \Tnew = \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}({\TcpOld[ij]} \cdot S_{ij}) +\mathop{\min_{j=1,\dots,M}} (\Tcm[hj]) \end{equation} - +% where $N$ is the number of clusters in the grid, $M$ is the number of nodes in each cluster, $\TcpOld[ij]$ is the computation time of processor $j$ in the cluster $i$ and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ during the -first iteration. The model computes the maximum computation time with scaling factor -from each node added to the communication time of the slowest node in the slowest cluster $h$. -It means only the communication time without any slack time is taken into account. +first iteration. The execution time for one iteration is equal to the sum of the maximum computation time for all nodes with the new scaling factors +and the slowest communication time without slack time during one iteration. +The latter is equal to the communication time of the slowest node in the slowest cluster $h$. +It means that only the communication time without any slack time is taken into account. Therefore, the execution time of the iterative application is equal to the execution time of one iteration as in (\ref{eq:perf}) multiplied by the number of iterations of that application. @@ -431,7 +438,7 @@ communication and no slack time. The main objective of DVFS operation is to reduce the overall energy consumption~\cite{Le_DVFS.Laws.of.Diminishing.Returns}. The operational frequency $F$ depends linearly on the supply voltage $V$, i.e., $V = \beta \cdot -F$ with some constant $\beta$.~This equation is used to study the change of the +F$ with some constant $\beta$. This equation is used to study the change of the dynamic voltage with respect to various frequency values in~\cite{Rauber_Analytical.Modeling.for.Energy}. The reduction process of the frequency can be expressed by the scaling factor $S$ which is the ratio between @@ -531,12 +538,13 @@ increasing significantly the execution time. In our previous works, \cite{Our_first_paper} and \cite{pdsec2015}, two methods that select the optimal frequency scaling factors for a homogeneous and a heterogeneous cluster respectively, were proposed. -Both methods selects the frequencies that gives the best tradeoff between +Both methods selects the frequencies that gives the best trade-off between energy consumption reduction and performance for message passing iterative synchronous applications. In this work we -are interested in grids that are composed of heterogeneous clusters were the nodes have different characteristics such as dynamic power, static power, computation power, frequencies range, network latency and bandwidth. -Due to the -heterogeneity of the processors, a vector of scaling factors should be selected +are interested in grids that are composed of heterogeneous clusters were the nodes +have different characteristics such as dynamic power, static power, computation power, +frequencies range, network latency and bandwidth. +Due to the heterogeneity of the processors, a vector of scaling factors should be selected and it must give the best trade-off between energy consumption and performance. The relation between the energy consumption and the execution time for an @@ -548,29 +556,31 @@ 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{equation} \label{eq:pnorm} \Pnorm = \frac{\Tnew}{\Told} \end{equation} - - -Where $Tnew$ is computed as in (\ref{eq:perf}) and $Told$ is computed as in (\ref{eq:told}) +% +where $Tnew$ is computed as in (\ref{eq:perf}) and $Told$ is computed as in (\ref{eq:told}). +% \begin{equation} \label{eq:told} \Told = \mathop{\max_{i=1,2,\dots,N}}_{j=1,2,\dots,M} (\Tcp[ij]+\Tcm[ij]) \end{equation} +% 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{equation} \label{eq:enorm} \Enorm = \frac{\Ereduced}{\Eoriginal} \end{equation} - -Where $\Ereduced$ is computed using (\ref{eq:energy}) and $\Eoriginal$ is +% +where $\Ereduced$ is computed using (\ref{eq:energy}) and $\Eoriginal$ is computed as in (\ref{eq:eorginal}). - - +% \begin{equation} \label{eq:eorginal} \Eoriginal = \sum_{i=1}^{N} \sum_{j=1}^{M} ( \Pd[ij] \cdot \Tcp[ij]) + @@ -579,9 +589,9 @@ computed as in (\ref{eq:eorginal}). While the main goal is to optimize the energy and execution time at the same time, the normalized energy and execution time curves do not evolve (increase/decrease) in the same way. -According to the equations~(\ref{eq:pnorm}) and (\ref{eq:enorm}), the -vector of frequency scaling factors $S_1,S_2,\dots,S_N$ reduce both the energy -and the execution time simultaneously. But the main objective is to produce +According to (\ref{eq:pnorm}) and (\ref{eq:enorm}), the +vector of frequency scaling factors $S_1,S_2,\dots,S_N$ reduces both the energy +and the execution time, but the main objective is to produce maximum energy reduction with minimum execution time reduction. This problem can be solved by making the optimization process for energy and @@ -597,9 +607,9 @@ equation, as follows: \begin{figure} \centering \subfloat[Homogeneous cluster]{% - \includegraphics[width=.4\textwidth]{fig/homo}\label{fig:r1}} \hspace{2cm}% + \includegraphics[width=.48\textwidth]{fig/homo}\label{fig:r1}} \hspace{0.4cm}% \subfloat[Heterogeneous grid]{% - \includegraphics[width=.4\textwidth]{fig/heter}\label{fig:r2}} + \includegraphics[width=.48\textwidth]{fig/heter}\label{fig:r2}} \label{fig:rel} \caption{The energy and performance relation} \end{figure} @@ -608,7 +618,7 @@ Then, the objective function can be modeled in order to find the maximum distance between the energy curve (\ref{eq:enorm}) and the performance curve (\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 +performance) at the same time, see Figure~\ref{fig:r1} and Figure~\ref{fig:r2}. Then the objective function has the following form: \begin{equation} \label{eq:max} @@ -644,7 +654,7 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin \item[{$\Ps[ij]$}] array of the static powers for all nodes. \item[{$\Fdiff[ij]$}] array of the differences between two successive frequencies for all nodes. \end{description} - \Ensure $\Sopt[11],\Sopt[12] \dots, \Sopt[NM_i]$, a vector of scaling factors that gives the optimal tradeoff between energy consumption and execution time + \Ensure $\Sopt[11],\Sopt[12] \dots, \Sopt[NM_i]$, a vector of scaling factors that gives the optimal trade-off between energy consumption and execution time \State $\Scp[ij] \gets \frac{\max_{i=1,2,\dots,N}(\max_{j=1,2,\dots,M_i}(\Tcp[ij]))}{\Tcp[ij]} $ \State $F_{ij} \gets \frac{\Fmax[ij]}{\Scp[i]},~{i=1,2,\cdots,N},~{j=1,2,\dots,M_i}.$ @@ -652,8 +662,8 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin \If{(not the first frequency)} \State $F_{ij} \gets F_{ij}+\Fdiff[ij],~i=1,\dots,N,~{j=1,\dots,M_i}.$ \EndIf - \State $\Told \gets $ computed as in equations (\ref{eq:told}). - \State $\Eoriginal \gets $ computed as in equations (\ref{eq:eorginal}) . + \State $\Told \gets $ computed as in Equation \ref{eq:told}. + \State $\Eoriginal \gets $ computed as in Equation \ref{eq:eorginal}. \State $\Sopt[ij] \gets 1,~i=1,\dots,N,~{j=1,\dots,M_i}. $ \State $\Dist \gets 0 $ \While {(all nodes have not reached their minimum \newline\hspace*{2.5em} frequency \textbf{or} $\Pnorm - \Enorm < 0 $)} @@ -661,8 +671,8 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin \State $F_{ij} \gets F_{ij} - \Fdiff[ij],~{i=1,\dots,N},~{j=1,\dots,M_i}$. \State $S_{ij} \gets \frac{\Fmax[ij]}{F_{ij}},~{i=1,\dots,N},~{j=1,\dots,M_i}.$ \EndIf - \State $\Tnew \gets $ computed as in equations (\ref{eq:perf}). - \State $\Ereduced \gets $ computed as in equations (\ref{eq:energy}). + \State $\Tnew \gets $ computed as in Equation \ref{eq:perf}. + \State $\Ereduced \gets $ computed as in Equation \ref{eq:energy}. \State $\Pnorm \gets \frac{\Told}{\Tnew}$, $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$ \If{$(\Pnorm - \Enorm > \Dist)$} \State $\Sopt[ij] \gets S_{ij},~i=1,\dots,N,~j=1,\dots,M_i. $ @@ -682,8 +692,7 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin \State Computations section. \State Communications section. \If {$(k=1)$} - \State Gather all times of computation and\newline\hspace*{3em}% - communication from each node. + \State Gather all times of computation and communication from each node. \State Call Algorithm \ref{HSA}. \State Compute the new frequencies from the\newline\hspace*{3em}% returned optimal scaling factors. @@ -696,7 +705,7 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin \end{algorithm} -In this section, the scaling factors selection algorithm for grids, algorithm~\ref{HSA}, +In this section, the scaling factors selection algorithm for grids, Algorithm~\ref{HSA}, is presented. It selects the vector of the frequency scaling factors that gives the best trade-off between minimizing the energy consumption and maximizing the performance of a message passing @@ -733,7 +742,7 @@ frequency scaling factors are computed as a ratio between the computation time of the slowest node and the computation time of the node $i$ as follows: \begin{equation} \label{eq:Scp} - \Scp[ij] = \frac{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]} + \Scp[ij] = \frac{ \mathop{\max\limits_{i=1,\dots N}}\limits_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]} \end{equation} Using the initial frequency scaling factors computed in (\ref{eq:Scp}), the algorithm computes the initial frequencies for all nodes as a ratio between the @@ -763,11 +772,13 @@ Therefore, the algorithm iterates on all remaining frequencies, from the higher bound until all nodes reach their minimum frequencies or their lower bounds, to compute the overall energy consumption and performance and selects the optimal vector of the frequency scaling factors. At each iteration the algorithm determines the slowest node -according to the equation (\ref{eq:perf}) and keeps its frequency unchanged, +according to Equation~\ref{eq:perf} +%\AG[]{Be consistent: remove word ``Equation'' and add parentheses around equation number, here and all along the rest of the text.} +and keeps its frequency unchanged, while it lowers the frequency of all other nodes by one gear. The new overall energy consumption and execution time are computed according to the new scaling factors. The optimal set of frequency scaling factors is the set that gives the -highest distance according to the objective function (\ref{eq:max}). +highest distance according to the objective function~\ref{eq:max}. Figures~\ref{fig:r1} and \ref{fig:r2} illustrate the normalized performance and consumed energy for an application running on a homogeneous cluster and a @@ -777,35 +788,32 @@ factor should start from the maximum frequency because the performance and the consumed energy decrease from the beginning of the plot. On the other hand, in the grid platform the performance is maintained at the beginning of the plot even if the frequencies of the faster nodes decrease until the computing -power of scaled down nodes are lower than the slowest node. In other words, -until they reach the higher bound. It can also be noticed that the higher the -difference between the faster nodes and the slower nodes is, the bigger the -maximum distance between the energy curve and the performance curve is, which results in bigger energy savings. +power of scaled down nodes are lower than the slowest node. It can also be noticed that the higher the difference between the faster nodes and the slower nodes is, the bigger the maximum distance between the energy curve and the performance curve is, which results in bigger energy savings. \section{Experimental results} \label{sec.expe} While in~\cite{pdsec2015} the energy model and the scaling factors selection algorithm were applied to a heterogeneous cluster and evaluated over the SimGrid simulator~\cite{SimGrid}, -in this paper real experiments were conducted over the grid'5000 platform. +in this paper real experiments were conducted over the Grid'5000 platform. -\subsection{Grid'5000 architature and power consumption} +\subsection{Grid'5000 architecture and power consumption} \label{sec.grid5000} -Grid'5000~\cite{grid5000} is a large-scale testbed that consists of ten sites distributed over all metropolitan France and Luxembourg. All the sites are connected together via a special long distance network called RENATER, +Grid'5000~\cite{grid5000} is a large-scale testbed that consists of ten sites distributed all over metropolitan France and Luxembourg. All the sites are connected together via a special long distance network called RENATER, which is the French National Telecommunication Network for Technology. -Each site of the grid is composed of few heterogeneous +Each site of the grid is composed of a few heterogeneous computing clusters and each cluster contains many homogeneous nodes. In total, -grid'5000 has about one thousand heterogeneous nodes and eight thousand cores. In each site, +Grid'5000 has about one thousand heterogeneous nodes and eight thousand cores. In each site, the clusters and their nodes are connected via high speed local area networks. Two types of local networks are used, Ethernet or Infiniband networks which have different characteristics in terms of bandwidth and latency. -Since grid'5000 is dedicated for testing, contrary to production grids it allows a user to deploy its own customized operating system on all the booked nodes. The user could have root rights and thus apply DVFS operations while executing a distributed application. Moreover, the grid'5000 testbed provides at some sites a power measurement tool to capture -the power consumption for each node in those sites. The measured power is the overall consumed power by by all the components of a node at a given instant, such as CPU, hard drive, main-board, memory, ... For more details refer to -\cite{Energy_measurement}. To just measure the CPU power of one core in a node $j$, - firstly, the power consumed by the node while being idle at instant $y$, noted as $\Pidle[jy]$, was measured. Then, the power was measured while running a single thread benchmark with no communication (no idle time) over the same node with its CPU scaled to the maximum available frequency. The latter power measured at time $x$ with maximum frequency for one core of node $j$ is noted $\Pmax[jx]$. The difference between the two measured power consumption represents the -dynamic power consumption of that core with the maximum frequency, see figure(\ref{fig:power_cons}). +Since Grid'5000 is dedicated to testing, contrary to production grids it allows a user to deploy its own customized operating system on all the booked nodes. The user could have root rights and thus apply DVFS operations while executing a distributed application. Moreover, the Grid'5000 testbed provides at some sites a power measurement tool to capture +the power consumption for each node in those sites. The measured power is the overall consumed power by all the components of a node at a given instant, such as CPU, hard drive, main-board, memory, \dots{} For more details refer to +\cite{Energy_measurement}. In order to correctly measure the CPU power of one core in a node $j$, + firstly, the power consumed by the node while being idle at instant $y$, noted as $\Pidle[jy]$, was measured. Then, the power was measured while running a single thread benchmark with no communication (no idle time) over the same node with its CPU scaled to the maximum available frequency. The latter power measured at time $x$ with maximum frequency for one core of node $j$ is noted $\Pmax[jx]$. The difference between the two measured power consumptions represents the +dynamic power consumption of that core with the maximum frequency, see Figure~\ref{fig:power_cons}. -The dynamic power $\Pd[j]$ is computed as in equation (\ref{eq:pdyn}) +The dynamic power $\Pd[j]$ is computed as in Equation~\ref{eq:pdyn} \begin{equation} \label{eq:pdyn} \Pd[j] = \max_{x=\beta_1,\dots \beta_2} (\Pmax[jx]) - \min_{y=\Theta_1,\dots \Theta_2} (\Pidle[jy]) @@ -817,35 +825,37 @@ $\lbrace\Theta_1,\Theta_2\rbrace$ is the time interval for the measured idle po Therefore, the dynamic power of one core is computed as the difference between the maximum measured value in maximum powers vector and the minimum measured value in the idle powers vector. -On the other hand, the static power consumption by one core is a part of the measured idle power consumption of the node. Since in grid'5000 there is no way to measure precisely the consumed static power and in~\cite{Our_first_paper,pdsec2015,Rauber_Analytical.Modeling.for.Energy} it was assumed that the static power represents a ratio of the dynamic power, the value of the static power is assumed as 20\% of dynamic power consumption of the core. +On the other hand, the static power consumption by one core is a part of the measured idle power consumption of the node. Since in Grid'5000 there is no way to measure precisely the consumed static power and in~\cite{Our_first_paper,pdsec2015,Rauber_Analytical.Modeling.for.Energy} it was assumed that the static power represents a ratio of the dynamic power, the value of the static power is assumed as 20\% of dynamic power consumption of the core. -In the experiments presented in the following sections, two sites of grid'5000 were used, Lyon and Nancy sites. These two sites have in total seven different clusters as in figure (\ref{fig:grid5000}). +In the experiments presented in the following sections, two sites of Grid'5000 were used, Lyon and Nancy sites. These two sites have in total seven different clusters as shown on Figure~\ref{fig:grid5000}. Four clusters from the two sites were selected in the experiments: one cluster from -Lyon's site, Taurus cluster, and three clusters from Nancy's site, Graphene, +Lyon's site, Taurus, and three clusters from Nancy's site, Graphene, Griffon and Graphite. Each one of these clusters has homogeneous nodes inside, while nodes from different clusters are heterogeneous in many aspects such as: computing power, power consumption, available -frequency ranges and local network features: the bandwidth and the latency. Table \ref{table:grid5000} shows -the details characteristics of these four clusters. Moreover, the dynamic powers were computed using the equation (\ref{eq:pdyn}) for all the nodes in the -selected clusters and are presented in table \ref{table:grid5000}. +frequency ranges and local network features: the bandwidth and the latency. Table~\ref{table:grid5000} shows +the detailed characteristics of these four clusters. Moreover, the dynamic powers were computed using Equation~\ref{eq:pdyn} for all the nodes in the +selected clusters and are presented in Table~\ref{table:grid5000}. \begin{figure}[!t] \centering \includegraphics[scale=1]{fig/grid5000} - \caption{The selected two sites of grid'5000} + \caption{The selected two sites of Grid'5000} \label{fig:grid5000} \end{figure} \begin{figure}[!t] \centering \includegraphics[scale=0.6]{fig/power_consumption.pdf} + \AG{I don't understand the labels on the horizontal axis: 10:30:37, 10:30:38, + etc.} \caption{The power consumption by one core from the Taurus cluster} \label{fig:power_cons} \end{figure} -The energy model and the scaling factors selection algorithm were applied to the NAS parallel benchmarks v3.3 \cite{NAS.Parallel.Benchmarks} and evaluated over grid'5000. +The energy model and the scaling factors selection algorithm were applied to the NAS parallel benchmarks v3.3 \cite{NAS.Parallel.Benchmarks} and evaluated over Grid'5000. The benchmark suite contains seven applications: CG, MG, EP, LU, BT, SP and FT. These applications have different computations and communications ratios and strategies which make them good testbed applications to evaluate the proposed algorithm and energy model. -The benchmarks have seven different classes, S, W, A, B, C, D and E, that represent the size of the problem that the method solves. In this work, the class D was used for all benchmarks in all the experiments presented in the next sections. +The benchmarks have seven different classes, S, W, A, B, C, D and E, that represent the size of the problem that the method solves. In this work, class D was used for all benchmarks in all the experiments presented in the next sections. @@ -855,25 +865,25 @@ The benchmarks have seven different classes, S, W, A, B, C, D and E, that repres \centering \begin{tabular}{|*{7}{c|}} \hline - Cluster & CPU & Max & Min & Diff. & no. of cores & dynamic power \\ - Name & model & Freq. & Freq. & Freq. & per CPU & of one core \\ - & & GHz & GHz & GHz & & \\ + & & Max & Min & Diff. & & \\ + Cluster & CPU & Freq. & Freq. & Freq. & No. of cores & Dynamic power \\ + Name & model & GHz & GHz & GHz & per CPU & of one core \\ \hline - & Intel & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\ - Taurus & Xeon & & & & & \\ - & E5-2630 & & & & & \\ + & Intel & & & & & \\ + Taurus & Xeon & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\ + & E5-2630 & & & & & \\ \hline - & Intel & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\ - Graphene & Xeon & & & & & \\ - & X3440 & & & & & \\ + & Intel & & & & & \\ + Graphene & Xeon & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\ + & X3440 & & & & & \\ \hline - & Intel & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\ - Griffon & Xeon & & & & & \\ - & L5420 & & & & & \\ + & Intel & & & & & \\ + Griffon & Xeon & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\ + & L5420 & & & & & \\ \hline - & Intel & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\ - Graphite & Xeon & & & & & \\ - & E5-2650 & & & & & \\ + & Intel & & & & & \\ + Graphite & Xeon & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\ + & E5-2650 & & & & & \\ \hline \end{tabular} \label{table:grid5000} @@ -887,23 +897,23 @@ In this section, the results of the application of the scaling factors selection to the NAS parallel benchmarks are presented. As mentioned previously, the experiments -were conducted over two sites of grid'5000, Lyon and Nancy sites. +were conducted over two sites of Grid'5000, Lyon and Nancy sites. Two scenarios were considered while selecting the clusters from these two sites : \begin{itemize} \item In the first scenario, nodes from two sites and three heterogeneous clusters were selected. The two sites are connected via a long distance network. -\item In the second scenario nodes from three clusters that are located in one site, Nancy site. +\item In the second scenario nodes from three clusters located in one site, Nancy site, were selected. \end{itemize} The main reason -behind using these two scenarios is to evaluate the influence of long distance communications (higher latency) on the performance of the +for using these two scenarios is to evaluate the influence of long distance communications (higher latency) on the performance of the scaling factors selection algorithm. Indeed, in the first scenario the computations to communications ratio -is very low due to the higher communication times which reduces the effect of DVFS operations. +is very low due to the higher communication times which reduce the effect of DVFS operations. The NAS parallel benchmarks are executed over -16 and 32 nodes for each scenario. The number of participating computing nodes form each cluster -are different because all the selected clusters do not have the same available number of nodes and all benchmarks do not require the same number of computing nodes. -Table \ref{tab:sc} shows the number of nodes used from each cluster for each scenario. +16 and 32 nodes for each scenario. The number of participating computing nodes from each cluster +is different because all the selected clusters do not have the same available number of nodes and all benchmarks do not require the same number of computing nodes. +Table~\ref{tab:sc} shows the number of nodes used from each cluster for each scenario. \begin{table}[h] @@ -938,16 +948,16 @@ Table \ref{tab:sc} shows the number of nodes used from each cluster for each sce The NAS parallel benchmarks are executed over these two platforms - with different number of nodes, as in Table \ref{tab:sc}. + with different number of nodes, as in Table~\ref{tab:sc}. The overall energy consumption of all the benchmarks solving the class D instance and using the proposed frequency selection algorithm is measured -using the equation of the reduced energy consumption, equation -(\ref{eq:energy}). This model uses the measured dynamic and static -power values showed in Table \ref{table:grid5000}. The execution +using the equation of the reduced energy consumption, Equation~\ref{eq:energy}. This model uses the measured dynamic power showed in Table~\ref{table:grid5000} +and the static +power is assumed to be equal to 20\% of the dynamic power. The execution time is measured for all the benchmarks over these different scenarios. The energy consumptions and the execution times for all the benchmarks are -presented in the plots \ref{fig:eng_sen} and \ref{fig:time_sen} respectively. +presented in Figures~\ref{fig:eng_sen} and \ref{fig:time_sen} respectively. For the majority of the benchmarks, the energy consumed while executing the NAS benchmarks over one site scenario for 16 and 32 nodes is lower than the energy consumed while using two sites. @@ -955,24 +965,27 @@ The long distance communications between the two distributed sites increase the The execution times of these benchmarks over one site with 16 and 32 nodes are also lower when compared to those of the two sites -scenario. Moreover, most of the benchmarks running over the one site scenario their execution times are approximately divided by two when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes). - -However, the execution times and the energy consumptions of EP and MG benchmarks, which have no or small communications, are not significantly affected - in both scenarios. Even when the number of nodes is doubled. On the other hand, the communications of the rest of the benchmarks increases when using long distance communications between two sites or increasing the number of computing nodes. +scenario. Moreover, most of the benchmarks running over the one site scenario their execution times are approximately divided by two when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes).\AG{Parse error (cannot understand the previous sentence).} +However, the execution times and the energy consumptions of EP and MG +benchmarks, which have no or small communications, are not significantly +affected in both scenarios, even when the number of nodes is doubled. On the +other hand, the communication times of the rest of the benchmarks increases when +using long distance communications between two sites or increasing the number of +computing nodes. The energy saving percentage is computed as the ratio between the reduced -energy consumption, equation (\ref{eq:energy}), and the original energy consumption, -equation (\ref{eq:eorginal}), for all benchmarks as in figure \ref{fig:eng_s}. +energy consumption, Equation~\ref{eq:energy}, and the original energy consumption, +Equation~\ref{eq:eorginal}, for all benchmarks as in Figure~\ref{fig:eng_s}. This figure shows that the energy saving percentages of one site scenario for 16 and 32 nodes are bigger than those of the two sites scenario which is due to the higher computations to communications ratio in the first scenario -than in the second one. Moreover, the frequency selecting algorithm selects smaller frequencies when the computations times are bigger than the communication times which +than in the second one. Moreover, the frequency selecting algorithm selects smaller frequencies when the computation times are bigger than the communication times which results in a lower energy consumption. Indeed, the dynamic consumed power -is exponentially related to the CPU's frequency value. On the other side, the increase in the number of computing nodes can +is exponentially related to the CPU's frequency value. On the other hand, the increase in the number of computing nodes can increase the communication times and thus produces less energy saving depending on the -benchmarks being executed. The results of the benchmarks CG, MG, BT and FT show more +benchmarks being executed. The results of benchmarks CG, MG, BT and FT show more energy saving percentage in one site scenario when executed over 16 nodes comparing to 32 nodes. While, LU and SP consume more energy with 16 nodes than 32 in one site because their computations to communications ratio is not affected by the increase of the number of local communications. \begin{figure} \centering @@ -989,11 +1002,11 @@ energy saving percentage in one site scenario when executed over 16 nodes compar The energy saving percentage is reduced for all the benchmarks because of the long distance communications in the two sites -scenario, except for the EP benchmark which has no communications. Therefore, the energy saving percentage of this benchmark is +scenario, except for the EP benchmark which has no communication. Therefore, the energy saving percentage of this benchmark is dependent on the maximum difference between the computing powers of the heterogeneous computing nodes, for example -in the one site scenario, the graphite cluster is selected but in the two sits scenario -this cluster is replaced with Taurus cluster which is more powerful. -Therefore, the energy saving of EP benchmarks are bigger in the two sites scenario due +in the one site scenario, the graphite cluster is selected but in the two sites scenario +this cluster is replaced with the Taurus cluster which is more powerful. +Therefore, the energy savings of the EP benchmark are bigger in the two sites scenario due to the higher maximum difference between the computing powers of the nodes. In fact, high differences between the nodes' computing powers make the proposed frequencies selecting @@ -1001,23 +1014,23 @@ algorithm select smaller frequencies for the powerful nodes which produces less energy consumption and thus more energy saving. The best energy saving percentage was obtained in the one site scenario with 16 nodes, the energy consumption was on average reduced up to 30\%. -\begin{figure} +\begin{figure*}[t] \centering \subfloat[The energy reduction while executing the NAS benchmarks over different scenarios ]{% \includegraphics[width=.48\textwidth]{fig/eng_s.eps}\label{fig:eng_s}} \hspace{0.4cm}% \subfloat[The performance degradation of the NAS benchmarks over different scenarios]{% \includegraphics[width=.48\textwidth]{fig/per_d.eps}\label{fig:per_d}}\hspace{0.4cm}% - \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks + \subfloat[The trade-off distance between the energy reduction and the performance of the NAS benchmarks over different scenarios]{% \includegraphics[width=.48\textwidth]{fig/dist.eps}\label{fig:dist}} \label{fig:exp-res} \caption{The experimental results of different scenarios} -\end{figure} +\end{figure*} Figure \ref{fig:per_d} presents the performance degradation percentages for all benchmarks over the two scenarios. The performance degradation percentage for the benchmarks running on two sites with 16 or 32 nodes is on average equal to 8.3\% or 4.7\% respectively. For this scenario, the proposed scaling algorithm selects smaller frequencies for the executions with 32 nodes without significantly degrading their performance because the communication times are higher with 32 nodes which results in smaller computations to communications ratio. On the other hand, the performance degradation percentage for the benchmarks running on one site with -16 or 32 nodes is on average equal to 3.2\% or 10.6\% respectively. In opposition to the two sites scenario, when the number of computing nodes is increased in the one site scenario, the performance degradation percentage is increased. Therefore, doubling the number of computing +16 or 32 nodes is on average equal to 3.2\% or 10.6\% respectively. In contrary to the two sites scenario, when the number of computing nodes is increased in the one site scenario, the performance degradation percentage is increased. Therefore, doubling the number of computing nodes when the communications occur in high speed network does not decrease the computations to communication ratio. @@ -1027,13 +1040,13 @@ performance degradation percentage only depends on the frequencies values select The rest of the benchmarks showed different performance degradation percentages, which decrease when the communication times increase and vice versa. -Figure \ref{fig:dist} presents the distance percentage between the energy saving and the performance degradation for each benchmark over both scenarios. The tradeoff distance percentage can be -computed as in equation \ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance -tradeoff, on average it is equal to 26.8\%. The one site scenario using both 16 and 32 nodes had better energy and performance -tradeoff comparing to the two sites scenario because the former has high speed local communications +Figure \ref{fig:dist} presents the distance percentage between the energy saving and the performance degradation for each benchmark over both scenarios. The trade-off distance percentage can be +computed as in Equation~\ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance +trade-off, on average it is equal to 26.8\%. The one site scenario using both 16 and 32 nodes had better energy and performance +trade-off comparing to the two sites scenario because the former has high speed local communications which increase the computations to communications ratio and the latter uses long distance communications which decrease this ratio. - Finally, the best energy and performance tradeoff depends on all of the following: + Finally, the best energy and performance trade-off depends on all of the following: 1) the computations to communications ratio when there are communications and slack times, 2) the heterogeneity of the computing powers of the nodes and 3) the heterogeneity of the consumed static and dynamic powers of the nodes. @@ -1042,17 +1055,18 @@ which increase the computations to communications ratio and the latter uses lon \subsection{The experimental results over multi-cores clusters} \label{sec.res-mc} -The clusters of grid'5000 have different number of cores embedded in their nodes -as shown in Table \ref{table:grid5000}. In -this section, the proposed scaling algorithm is evaluated over the grid'5000 platform while using multi-cores nodes selected according to the one site scenario described in the section \ref{sec.res}. +The clusters of Grid'5000 have different number of cores embedded in their nodes +as shown in Table~\ref{table:grid5000}. In +this section, the proposed scaling algorithm is evaluated over the Grid'5000 platform while using multi-cores nodes selected according to the one site scenario described in Section~\ref{sec.res}. The one site scenario uses 32 cores from multi-cores nodes instead of 32 distinct nodes. For example if the participating number of cores from a certain cluster is equal to 14, in the multi-core scenario the selected nodes is equal to 4 nodes while using 3 or 4 cores from each node. The platforms with one -core per node and multi-cores nodes are shown in Table \ref{table:sen-mc}. -The energy consumptions and execution times of running the class D of the NAS parallel -benchmarks over these four different scenarios are presented -in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively. +core per node and multi-cores nodes are shown in Table~\ref{table:sen-mc}. +The energy consumptions and execution times of running class D of the NAS parallel +benchmarks over these two different scenarios are presented +in Figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively. + \begin{table}[] \centering @@ -1095,9 +1109,20 @@ the one site one core scenario when compared to the ratio of the multi-cores sc More energy reduction can be gained when this ratio is big because it pushes the proposed scaling algorithm to select smaller frequencies that decrease the dynamic power consumption. These experiments also showed that the energy consumption and the execution times of the EP and MG benchmarks do not change significantly over these two scenarios because there are no or small communications. Contrary to EP and MG, the energy consumptions and the execution times of the rest of the benchmarks vary according to the communication times that are different from one scenario to the other. +\begin{figure*}[t] + \centering + \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{% + \includegraphics[width=.48\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{0.4cm}% + \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios + ]{% + \includegraphics[width=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}% + \subfloat[The trade-off distance of running NAS benchmarks over one core and multicores scenarios]{% + \includegraphics[width=.48\textwidth]{fig/dist_mc.eps}\label{fig:dist-mc}} + \label{fig:exp-res2} + \caption{The experimental results of one core and multi-cores scenarios} +\end{figure*} - -The energy saving percentages of all NAS benchmarks running over these two scenarios are presented in the figure \ref{fig:eng-s-mc}. +The energy saving percentages of all NAS benchmarks running over these two scenarios are presented in Figure~\ref{fig:eng-s-mc}. The figure shows that the energy saving percentages in the one core and the multi-cores scenarios are approximately equivalent, on average they are equal to 25.9\% and 25.1\% respectively. @@ -1105,51 +1130,40 @@ The energy consumption is reduced at the same rate in the two scenarios when com The performance degradation percentages of the NAS benchmarks are presented in -figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages is higher for the NAS benchmarks over the one core per node scenario (on average equal to 10.6\%) than over the multi-cores scenario (on average equal to 7.5\%). The performance degradation percentages over the multi-cores scenario is lower because the computations to communications ratio is smaller than the ratio of the other scenario. +Figure~\ref{fig:per-d-mc}. It shows that the performance degradation percentages are higher for the NAS benchmarks over the one core per node scenario (on average equal to 10.6\%) than over the multi-cores scenario (on average equal to 7.5\%). The performance degradation percentages over the multi-cores scenario are lower because the computations to communications ratios are smaller than the ratios of the other scenario. -The tradeoff distance percentages of the NAS benchmarks over the two scenarios are presented -in the figure \ref{fig:dist-mc}. These tradeoff distance between energy consumption reduction and performance are used to verify which scenario is the best in both terms at the same time. The figure shows that the tradeoff distance percentages are on average bigger over the multi-cores scenario (17.6\%) than over the one core per node scenario (15.3\%). +The trade-off distances percentages of the NAS benchmarks over the two scenarios are presented +in ~Figure~\ref{fig:dist-mc}. These trade-off distances between energy consumption reduction and performance are used to verify which scenario is the best in both terms at the same time. The figure shows that the trade-off distance percentages are on average bigger over the multi-cores scenario (17.6\%) than over the one core per node scenario (15.3\%). -\begin{figure} - \centering - \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{% - \includegraphics[width=.48\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{0.4cm}% - \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios - ]{% - \includegraphics[width=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}% - \subfloat[The tradeoff distance of running NAS benchmarks over one core and multicores scenarios]{% - \includegraphics[width=.48\textwidth]{fig/dist_mc.eps}\label{fig:dist-mc}} - \label{fig:exp-res} - \caption{The experimental results of one core and multi-cores scenarios} -\end{figure} -\subsection{Experiments with different static and dynamic powers consumption scenarios} + +\subsection{Experiments with different static power scenarios} \label{sec.pow_sen} -In section \ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU, the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too. +In Section~\ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU, the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too. The aim of this section is to evaluate the scaling algorithm while assuming different values of static powers. In addition to the previously used percentage of static power, two new static power ratios, 10\% and 30\% of the measured dynamic power of the core, are used in this section. The experiments have been executed with these two new static power scenarios over the one site one core per node scenario. -In these experiments, the class D of the NAS parallel benchmarks are executed over Nancy's site. 16 computing nodes from the three clusters, Graphite, Graphene and Griffon, where used in this experiment. +In these experiments, class D of the NAS parallel benchmarks are executed over the Nancy site. 16 computing nodes from the three clusters, Graphite, Graphene and Griffon, where used in this experiment. -\begin{figure} +\begin{figure*}[t] \centering \subfloat[The energy saving percentages for the nodes executing the NAS benchmarks over the three power scenarios]{% \includegraphics[width=.48\textwidth]{fig/eng_pow.eps}\label{fig:eng-pow}} \hspace{0.4cm}% \subfloat[The performance degradation percentages for the NAS benchmarks over the three power scenarios]{% \includegraphics[width=.48\textwidth]{fig/per_pow.eps}\label{fig:per-pow}}\hspace{0.4cm}% - \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios]{% + \subfloat[The trade-off distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios]{% \includegraphics[width=.48\textwidth]{fig/dist_pow.eps}\label{fig:dist-pow}} \label{fig:exp-pow} \caption{The experimental results of different static power scenarios} -\end{figure} +\end{figure*} @@ -1161,67 +1175,67 @@ In these experiments, the class D of the NAS parallel benchmarks are executed ov \end{figure} The energy saving percentages of the NAS benchmarks with the three static power scenarios are presented -in figure \ref{fig:eng_sen}. This figure shows that the 10\% of static power scenario +in Figure~\ref{fig:eng_sen}. This figure shows that the 10\% of static power scenario gives the biggest energy saving percentages in comparison to the 20\% and 30\% static power scenarios. The small value of the static power consumption makes the proposed scaling algorithm select smaller frequencies for the CPUs. These smaller frequencies reduce the dynamic energy consumption more than increasing the consumed static energy which gives less overall energy consumption. The energy saving percentages of the 30\% static power scenario is the smallest between the other scenarios, because the scaling algorithm selects bigger frequencies for the CPUs which increases the energy consumption. Figure \ref{fig:fre-pow} demonstrates that the proposed scaling algorithm selects the best frequency scaling factors according to the static power consumption ratio being used. -The performance degradation percentages are presented in the figure \ref{fig:per-pow}. +The performance degradation percentages are presented in Figure~\ref{fig:per-pow}. The 30\% static power scenario had less performance degradation percentage because the scaling algorithm had selected big frequencies for the CPUs. While, -the inverse happens in the 10\% and 20\% scenarios because the scaling algorithm had selected CPUs' frequencies smaller than those of the 30\% scenario. The tradeoff distance percentage for the NAS benchmarks with these three static power scenarios -are presented in the figure \ref{fig:dist}. -It shows that the best tradeoff +the inverse happens in the 10\% and 20\% scenarios because the scaling algorithm had selected CPUs' frequencies smaller than those of the 30\% scenario. The trade-off distance percentage for the NAS benchmarks with these three static power scenarios +are presented in Figure~\ref{fig:dist}. +It shows that the best trade-off distance percentage is obtained with the 10\% static power scenario and this percentage is decreased for the other two scenarios because the scaling algorithm had selected different frequencies according to the static power values. -In the EP benchmark, the energy saving, performance degradation and tradeoff -distance percentages for the these static power scenarios are not significantly different because there is no communication in this benchmark. Therefore, the static power is only consumed during computation and the proposed scaling algorithm selects similar frequencies for the three scenarios. On the other hand, for the rest of the benchmarks, the scaling algorithm selects the values of the frequencies according to the communication times of each benchmark because the static energy consumption increases proportionally to the communication times. +In the EP benchmark, the energy saving, performance degradation and trade-off +distance percentages for these static power scenarios are not significantly different because there is no communication in this benchmark. Therefore, the static power is only consumed during computation and the proposed scaling algorithm selects similar frequencies for the three scenarios. On the other hand, for the rest of the benchmarks, the scaling algorithm selects the values of the frequencies according to the communication times of each benchmark because the static energy consumption increases proportionally to the communication times. -\subsection{The comparison of the proposed frequencies selecting algorithm } +\subsection{Comparison of the proposed frequencies selecting algorithm } \label{sec.compare_EDP} -Finding the frequencies that gives the best tradeoff between the energy consumption and the performance for a parallel +Finding the frequencies that give the best trade-off between the energy consumption and the performance for a parallel application is not a trivial task. Many algorithms have been proposed to tackle this problem. In this section, the proposed frequencies selecting algorithm is compared to a method that uses the well known energy and delay product objective function, $EDP=energy \times delay$, that has been used by many researchers \cite{EDP_for_multi_processors,Energy_aware_application_scheduling,Exploring_Energy_Performance_TradeOffs}. This objective function was also used by Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS} where they select the frequencies that minimize the EDP product and apply them with DVFS operations to the multi-cores architecture. Their online algorithm predicts the energy consumption and execution time of a processor before using the EDP method. -To fairly compare the proposed frequencies scaling algorithm to Spiliopoulos et al. algorithm, called Maxdist and EDP respectively, both algorithms use the same energy model, equation \ref{eq:energy} and -execution time model, equation \ref{eq:perf}, to predict the energy consumption and the execution time for each computing node. -Moreover, both algorithms start the search space from the upper bound computed as in equation \ref{eq:Fint}. +To fairly compare the proposed frequencies scaling algorithm to Spiliopoulos et al. algorithm, called Maxdist and EDP respectively, both algorithms use the same energy model, Equation~\ref{eq:energy} and +execution time model, Equation~\ref{eq:perf}, to predict the energy consumption and the execution time for each computing node. +Moreover, both algorithms start the search space from the upper bound computed as in Equation~\ref{eq:Fint}. Finally, the resulting EDP algorithm is an exhaustive search algorithm that tests all the possible frequencies, starting from the initial frequencies (upper bound), and selects the vector of frequencies that minimize the EDP product. -Both algorithms were applied to the class D of the NAS benchmarks over 16 nodes. -The participating computing nodes are distributed according to the two scenarios described in section \ref{sec.res}. -The experimental results, the energy saving, performance degradation and tradeoff distance percentages, are -presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively. +Both algorithms were applied to class D of the NAS benchmarks over 16 nodes. +The participating computing nodes are distributed according to the two scenarios described in Section~\ref{sec.res}. +The experimental results, the energy saving, performance degradation and trade-off distance percentages, are +presented in Figures~\ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively. -\begin{figure} +\begin{figure*}[t] \centering \subfloat[The energy reduction induced by the Maxdist method and the EDP method]{% \includegraphics[width=.48\textwidth]{fig/edp_eng}\label{fig:edp-eng}} \hspace{0.4cm}% \subfloat[The performance degradation induced by the Maxdist method and the EDP method]{% \includegraphics[width=.48\textwidth]{fig/edp_per}\label{fig:edp-perf}}\hspace{0.4cm}% - \subfloat[The tradeoff distance between the energy consumption reduction and the performance for the Maxdist method and the EDP method]{% + \subfloat[The trade-off distance between the energy consumption reduction and the performance for the Maxdist method and the EDP method]{% \includegraphics[width=.48\textwidth]{fig/edp_dist}\label{fig:edp-dist}} \label{fig:edp-comparison} \caption{The comparison results} -\end{figure} +\end{figure*} As shown in these figures, the proposed frequencies selection algorithm, Maxdist, outperforms the EDP algorithm in terms of energy consumption reduction and performance for all of the benchmarks executed over the two scenarios. The proposed algorithm gives better results than EDP because it maximizes the energy saving and the performance at the same time. Moreover, the proposed scaling algorithm gives the same weight for these two metrics. -Whereas, the EDP algorithm gives sometimes negative tradeoff values for some benchmarks in the two sites scenarios. -These negative tradeoff values mean that the performance degradation percentage is higher than energy saving percentage. -The high positive values of the tradeoff distance percentage mean that the energy saving percentage is much higher than the performance degradation percentage. +Whereas, the EDP algorithm gives sometimes negative trade-off values for some benchmarks in the two sites scenarios. +These negative trade-off values mean that the performance degradation percentage is higher than the energy saving percentage. +The high positive values of the trade-off distance percentage mean that the energy saving percentage is much higher than the performance degradation percentage. The time complexity of both Maxdist and EDP algorithms are $O(N \cdot M \cdot F)$ and $O(N \cdot M \cdot F^2)$ respectively, where $N$ is the number of the clusters, $M$ is the number of nodes and $F$ is the maximum number of available frequencies. When Maxdist is applied to a benchmark that is being executed over 32 nodes distributed between Nancy and Lyon sites, it takes on average $0.01 ms$ to compute the best frequencies while EDP is on average ten times slower over the same architecture. @@ -1229,27 +1243,26 @@ maximum number of available frequencies. When Maxdist is applied to a benchmark \section{Conclusion} \label{sec.concl} -This paper has presented a new online frequencies selection algorithm. +This paper presents a new online frequencies selection algorithm. The algorithm selects the best vector of -frequencies that maximizes the tradeoff distance +frequencies that maximizes the trade-off distance between the predicted energy consumption and the predicted execution time of the distributed iterative applications running over a heterogeneous grid. A new energy model is used by the proposed algorithm to predict the energy consumption of the distributed iterative message passing application running over a grid architecture. To evaluate the proposed method on a real heterogeneous grid platform, it was applied on the - NAS parallel benchmarks and the class D instance was executed over the grid'5000 testbed platform. - The experimental results showed that the algorithm reduces on average 30\% of the energy consumption -for all the NAS benchmarks while only degrading by 3.2\% on average the performance. -The Maxdist algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters' sites or use multi-cores per node architecture or consume different static power values. The algorithm selects different vector of frequencies according to the + NAS parallel benchmarks and the class D instance was executed over the Grid'5000 testbed platform. + The experiments on 16 nodes, distributed over three clusters, showed that the algorithm on average reduces by 30\% the energy consumption +for all the NAS benchmarks while on average only degrading by 3.2\% the performance. +The Maxdist algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters' sites or use multi-cores per node architecture or consume different static power values. The algorithm selects different vectors of frequencies according to the computations and communication times ratios, and the values of the static and measured dynamic powers of the CPUs. Finally, the proposed algorithm was compared to another method that uses the well known energy and delay product as an objective function. The comparison results showed -that the proposed algorithm outperforms the latter by selecting a vector of frequencies that gives a better tradeoff between energy consumption reduction and performance. +that the proposed algorithm outperforms the latter by selecting a vector of frequencies that gives a better trade-off between energy consumption reduction and performance. In the near future, we would like to develop a similar method that is adapted to asynchronous iterative applications where iterations are not synchronized and communications are overlapped with computations. - The development of -such a method might require a new energy model because the +The development of such a 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. @@ -1262,9 +1275,20 @@ This work has been partially supported by the Labex ACTION project (contract Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for supporting his work. -\section*{References} +%\section*{References} \bibliography{my_reference} \end{document} - +%%% Local Variables: +%%% mode: latex +%%% TeX-master: t +%%% fill-column: 80 +%%% ispell-local-dictionary: "american" +%%% End: + +% LocalWords: DVFS Fanfakh Charr Franche Comté IUT Maréchal Juin cedex NAS et +% LocalWords: supercomputing Tianhe Shoubu ExaScaler RIKEN GFlops CPUs GPUs +% LocalWords: Luley Xeon NVIDIA GPU Rong Naveen Lizhe al AMD ij hj RENATER +% LocalWords: Infiniband Graphene consumptions versa multi Spiliopoulos Labex +% LocalWords: Maxdist ANR LABX