\maketitle
+
+\begin{abstract}
+\textcolor{blue}{
+ In recent years, green computing topic has being became an important topic in
+ the domain of the research. The increase in computing power of the computing
+ platforms is increased the energy consumption and the carbon dioxide emissions.
+ Many techniques have being used to minimize the cost of the energy consumption
+ and reduce environmental pollution. Dynamic voltage and frequency scaling (DVFS)
+ is one of these techniques. It used to reduce the power consumption of the CPU
+ while computing by lowering its frequency. Moreover, lowering the frequency of
+ a CPU may increase the execution time of an application running on that
+ processor. Therefore, the frequency that gives the best trade-off between
+ the energy consumption and the performance of an application must be selected.
+ In this paper, a new online frequency selecting algorithm for heterogeneous
+ grid (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best
+ trade-off between energy saving and performance degradation, for each node
+ computing the message passing iterative application. 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 heterogeneous
+ grid. The proposed algorithm is evaluated on real testbed, grid'5000 platform, while
+ running the NAS parallel benchmarks. The experiments show that it reduces the
+ energy consumption on average up to \np[\%]{30} while declines the performance
+ on average by \np[\%]{3}. Finally, the algorithm is
+ compared to an existing method, the comparison results show that it outperforms the
+ latter in term of energy and performance trade-off.}
+\end{abstract}
+
+
\section{Introduction}
\label{sec.intro}
-\textcolor{blue}{
+\textcolor{red}{did you verify that these informations are still accurate before changing the years to 2015?}
The need for more computing power is continually increasing. To partially
satisfy this need, most supercomputers constructors just put more computing
nodes in their platform. The resulting platforms may achieve higher floating
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
50.32 kilowatts.
-}
-\textcolor{blue}{
Besides platform improvements, there are many software and hardware techniques
to lower the energy consumption of these platforms, such as scheduling, DVFS,
\dots{} DVFS is a widely used process to reduce the energy consumption of a
\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 show significant energy
+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. In this paper, a new frequencies selecting algorithm
-adapted for heterogeneous grid platform is presented and executed over real testbed,
-the grid'5000 platform \cite{grid5000}. It selects the vector of
-frequencies, for a heterogeneous grid platform running a message passing iterative
-application, that simultaneously tries to offer 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.}
+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, 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.
+
-\textcolor{blue}{
This paper is organized as follows: Section~\ref{sec.relwork} presents some
related works from other authors. Section~\ref{sec.exe} describes how the
execution time of message passing programs can be predicted. It also presents
an energy model that predicts the energy consumption of an application running
-over a heterogeneous grid. Section~\ref{sec.compet} presents the
+over a grid platform. Section~\ref{sec.compet} presents the
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 a grid'5000 testbed.
+NAS parallel benchmarks and executing them on the grid'5000 testbed.
It shows the results of running different scenarios using multi-cores and one core per node
-and comparing them. It also shows the results of running
-three different power scenarios and comparing them. Moreover, it shows the
+and comparing them. It also evaluates the algorithm 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.}
+in Section~\ref{sec.concl} the paper ends with a summary and some future works.
\section{Related works}
\label{sec.relwork}
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, 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.
\begin{figure}
\centering
\label{fig:fre-pow}
\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
gives the biggest energy saving percentages in comparison to the 20\% and 30\% static power
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.
+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.
-\subsection{The comparison between the proposed frequencies selecting algorithm and the energy and delay product algorithm}
+\subsection{The 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
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 well known energy and delay product method, $EDP=energy \times delay$, that have been used by many researchers \cite{EDP_for_multi_processors,Energy_aware_application_scheduling,Exploring_Energy_Performance_TradeOffs}.
-This method 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
+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
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.
+presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
+
+
\begin{figure}
\centering
\includegraphics[scale=0.5]{fig/edp_eng}
\caption{Comparing of the tradeoff distance for the proposed method with EDP method}
\label{fig:edp-dist}
\end{figure}
-\textcolor{blue}{As shown form these figures, the proposed frequencies selection algorithm, Maxdist, outperform the EDP algorithm in term of energy and performance for all of the benchmarks executed over the two scenarios.
-Generally, the proposed algorithm gives better results for all benchmarks because it is
-optimized the distance between the energy saving and the performance degradation in the same time.
+
+
+
+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 some times negative tradeoff values for some benchmarks in the two sites scenarios.
+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 higher positive value of the tradeoff distance percentage mean that the energy saving percentage is much higher than the performance degradation percentage.
+The high positive values of the tradeoff 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. The proposed algorithm, Maxdist, has selected the best frequencies in a small execution time,
-on average is equal to 0.01 $ms$, when it is executed over 32 nodes distributed between Nancy and Lyon sites.
-While the EDP algorithm was slower than Maxdist algorithm by ten times over the same number of nodes and same distribution, its execution time on average
-is equal to 0.1 $ms$.
-}
+$O(N \cdot M \cdot F^2)$ 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.
+
\section{Conclusion}
\label{sec.concl}
-\textcolor{blue}{
-This paper has been presented a new online frequencies selection algorithm.
-It works based on objective function that maximized the tradeoff distance
+This paper has presented a new online frequencies selection algorithm.
+ The algorithm selects the best vector of
+frequencies that maximizes the tradeoff distance
between the predicted energy consumption and the predicted execution time of the distributed
-iterative applications running over heterogeneous grid. The algorithm selects the best vector of the
-frequencies which maximized the objective function has been used. A new energy model
-used by the proposed algorithm for measuring and predicting the energy consumption
-of the distributed iterative message passing application running over grid architecture.
+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 class D instance and executed over grid'5000 testbed platform.
-The experimental results showed that the algorithm saves the energy consumptions on average
-for all NAS benchmarks up to 30\% while gives only 3\% percentage on average for the performance
-degradation for the same instance. The algorithm also selecting different frequencies according to the
-computations and communication times ratio, and according to the values of the static and measured dynamic power of the CPUs. The computations to communications ratio was varied between different scenarios have been used, concerning to the distribution of the computing nodes between different clusters' sites and using one core or multi-cores per node.
-Finally, the proposed algorithm was compared to other algorithm which it
-used the will known energy and delay product as an objective function. The comparison results showed
-that the proposed algorithm outperform the other one in term of energy-time tradeoff.
+ 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\% 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, use one core or multi-cores per node or assume different values for the consumed static power. The algorithm selects different vector 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.
+
In the near future, we would like to develop a similar method that is adapted to
-asynchronous iterative applications where each task does not
-wait for other tasks to finish their works. The development of
+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
number of iterations is not known in advance and depends on
the global convergence of the iterative system.
-}
+
\section*{Acknowledgment}
This work has been partially supported by the Labex ACTION project (contract
-``ANR-11-LABX-01-01''). Computations have been performed on the supercomputer
-facilities of the Mésocentre de calcul de Franche-Comté. As a PhD student,
+``ANR-11-LABX-01-01''). Computations have been performed on the Grid'5000 platform. As a PhD student,
Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for
supporting his work.
