X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/bf9f640ce156a5494c865ea56de417e94038a582..bddd7466e25adccae000e02d733508c45f397bd0:/mpi-energy2-extension/Heter_paper.tex?ds=inline diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex index 5a598e9..31d1182 100644 --- a/mpi-energy2-extension/Heter_paper.tex +++ b/mpi-energy2-extension/Heter_paper.tex @@ -107,9 +107,9 @@ -\title{Energy Consumption Reduction with DVFS for Message \\ +\title{Optimizing Energy Consumption with DVFS for Message \\ Passing Iterative Applications on \\ - Grid Architecture} + Grid Architectures} @@ -200,17 +200,14 @@ 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 frequency selecting algorithm +\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 create homogeneous and -heterogeneous platforms and runs message passing parallel applications over -them. % -\AG{[\dots], which offers easy tools to describe homogeneous and heterogeneous - platforms, and to simulate the execution of message passing parallel - applications over them.}% +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 @@ -234,6 +231,7 @@ 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} @@ -394,7 +392,7 @@ and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ dur 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\AG[]{It means that\dots} only the communication time without any slack time is taken into account. +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. @@ -543,9 +541,10 @@ frequency scaling factors for a homogeneous and a heterogeneous cluster respecti 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 @@ -789,11 +788,7 @@ 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, -\AG[]{That's not a sentence.} -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} @@ -851,8 +846,6 @@ selected clusters and are presented in Table~\ref{table:grid5000}. \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} @@ -871,7 +864,7 @@ The benchmarks have seven different classes, S, W, A, B, C, D and E, that repres \begin{tabular}{|*{7}{c|}} \hline & & Max & Min & Diff. & & \\ - Cluster & CPU & Freq. & Freq. & Freq. & No. of cores & Dynamic power \\ + Cluster & CPU & Freq. & Freq. & Freq. & Cores & Dynamic power \\ Name & model & GHz & GHz & GHz & per CPU & of one core \\ \hline & Intel & & & & & \\ @@ -927,7 +920,7 @@ Table~\ref{tab:sc} shows the number of nodes used from each cluster for each sce \begin{tabular}{|*{4}{c|}} \hline \multirow{2}{*}{Scenario name} & \multicolumn{3}{c|} {The participating clusters} \\ \cline{2-4} - & Cluster & Site & No. of nodes \\ + & Cluster & Site & Nodes per cluster \\ \hline \multirow{3}{*}{Two sites / 16 nodes} & Taurus & Lyon & 5 \\ \cline{2-4} & Graphene & Nancy & 5 \\ \cline{2-4} @@ -970,17 +963,16 @@ 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).\AG{Parse error (cannot understand the previous sentence).} +scenario. Moreover, most of the benchmarks running over the one site scenario have their execution times 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\AG[]{the communication time?} of the rest of the benchmarks increases when +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}. @@ -1079,8 +1071,8 @@ in Figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively. \caption{The multicores scenarios} \begin{tabular}{|*{4}{c|}} \hline -Scenario name & Cluster name & \begin{tabular}[c]{@{}c@{}}No. of nodes\\ in each cluster\end{tabular} & - \begin{tabular}[c]{@{}c@{}}No. of cores\\ for each node\end{tabular} \\ \hline +Scenario name & Cluster name & Nodes per cluster & + Cores per node \\ \hline \multirow{3}{*}{One core per node} & Graphite & 4 & 1 \\ \cline{2-4} & Graphene & 14 & 1 \\ \cline{2-4} & Griffon & 14 & 1 \\ \hline