From: jean-claude Date: Fri, 31 Oct 2014 07:28:59 +0000 (+0100) Subject: Corrected some conflicts, paper corrected till line 656 X-Git-Tag: pdsec15_submission~85 X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/commitdiff_plain/cb70815089ce5015bfb95cf39990a9daafa03b0a?ds=sidebyside;hp=--cc Corrected some conflicts, paper corrected till line 656 Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/mpi-energy2 Conflicts: Heter_paper.tex --- cb70815089ce5015bfb95cf39990a9daafa03b0a diff --cc Heter_paper.tex index 7bd1de4,20ca229..024cc29 --- a/Heter_paper.tex +++ b/Heter_paper.tex @@@ -435,14 -419,31 +419,24 @@@ In figure (\ref{fig:st_freq}), the nod \section{Experimental results} \label{sec.expe} +To evaluate the efficiency and the overall energy consumption reduction of algorithm~\ref{HSA}), it was applied to the NAS parallel benchmarks NPB v3.3 +\cite{44}. The experiments were executed on the simulator SimGrid/SMPI +v3.10~\cite{casanova+giersch+legrand+al.2014.versatile} which offers easy tools to create a heterogeneous platform and run message passing applications over it. The heterogeneous platform that was used in the experiments, had one core per node because just one process was executed per node. The heterogeneous platform was composed of four types of nodes. Each type of nodes had different characteristics such as the maximum CPU frequency, the number of +available frequencies and the computational power, see table +(\ref{table:platform}). The characteristics of these different types of nodes are inspired from the specifications of real Intel processors. The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, for example if a benchmark was executed on 8 nodes, 2 nodes from each type were used. Since the constructors of CPUs do not specify the dynamic and the static power of their CPUs, for each type of node they were chosen proportionally to its computing power (FLOPS). In the initial heterogeneous platform, while computing with highest frequency, each node consumed power proportional to its computing power which 80\% of it was dynamic power and the rest was 20\% was static power, the same assumption was made in \cite{45,3}. Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwidth. -The experiments of this work are executed on the simulator SimGrid/SMPI -v3.10~\cite{casanova+giersch+legrand+al.2014.versatile}. We are configure the -simulator to use a heterogeneous cluster with one core per node. The proposed -heterogeneous cluster has four different types of nodes. Each node in the cluster -has different characteristics such as the maximum frequency speed, the number of -available frequencies and dynamic and static powers values, see table -(\ref{table:platform}). These different types of processing nodes are simulate some -real Intel processors. The maximum number of nodes that supported by the cluster -is 144 nodes according to characteristics of some MPI programs of the NAS -benchmarks v3.3 \cite{44} that used. We are use the same number from each type of nodes when we -run the iterative MPI programs, for example if we are execute the program on 8 node, there -are 2 nodes from each type participating in the computation. The dynamic and -static power values is different from one type to other. Each node has a dynamic -and static power values proportionally increased to their computing power (FLOPS), for more -details see the Intel data sheets in \cite{47}. Each node has a percentage of -80\% for dynamic power and 20\% for static power of the total power -consumption of a CPU, the same assumption is made in \cite{45,3}. These nodes are -connected via an ethernet network with 1 Gbit/s bandwidth. The proposed scaling algorithm has a small + +\textbf{modify the characteristics table by replacing the similar column with the computing power of the different types of nodes in flops} ++ ++ ++ The proposed scaling algorithm has a small + execution time: for a heterogeneous cluster composed of four different types of + nodes having the characteristics presented in table~(\ref{table:platform}), it -takes \np[ms]{0.04} on average for 4 nodes and \np[ms]{0.15} on average for 144 -nodes. The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the ++takes on average \np[ms]{0.04} for 4 nodes and \np[ms]{0.15} on average for 144 ++nodes to compute the best scaling factors vector. The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the + number of iterations and $N$ is the number of computing nodes. The algorithm -needs on average from 12 to 20 iterations to selects the best vector of frequency scaling factors that give the results of the next section. ++needs from 12 to 20 iterations to select the best vector of frequency scaling factors that gives the results of the next section. ++ \begin{table}[htb] \caption{Heterogeneous nodes characteristics} % title of Table @@@ -475,9 -476,10 +469,10 @@@ \subsection{The experimental results of the scaling algorithm} \label{sec.res} -The proposed algorithm was applied to seven MPI programs of the NAS benchmarks (EP, CG, MG, FT, BT, LU and SP) NPB v3.3, which were run with three classes (A, B and C). -In this experiments we are interested to run the class C, the biggest class compared to A and B, on different number of -nodes, from 4 to 144 nodes according to the type of the iterative MPI program. Depending on the proposed energy consumption model EQ(\ref{eq:energy}), - we are measure the energy consumption for all the NAS MPI programs. The dynamic and static power values are used under the same assumption used by \cite{45,3}, we are used a percentage of 80\% for dynamic power and 20\% for static of the total power consumption of a CPU. The heterogeneous nodes in table (\ref{table:platform}) have different simulated computing power (FLOPS), ranked from the node of type 1 with smaller computing power (FLOPS) to the highest computing power (FLOPS) for node of type 4. Therefore, the power values are used proportionally increased from nodes of type 1 to nodes of type 4 that with highest computing power. Then, we are used an assumption that the power consumption is increased linearly when the computing power (FLOPS) of the processor is increased, see \cite{48}. ++<<<<<<< HEAD +The proposed algorithm was applied to the seven parallel NAS benchmarks (EP, CG, MG, FT, BT, LU and SP) and the benchmarks were executed with the three classes: A,B and C. However, due to the lack of space in this paper, only the results of the biggest class, C, are presented while being run on different number of nodes, ranging from 4 to 128 or 144 nodes depending on the benchmark being executed. + + \begin{table}[htb] \caption{Running NAS benchmarks on 4 nodes }