X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/5c75116d9c7d80cb7029caf6307469d4b5abc9ed..e2969fac86b461eb0348ae53694e9ef47cc88620:/mpi-energy2-extension/Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index d313a42..cd87024 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -87,7 +87,7 @@
 
 
 \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.
@@ -98,7 +98,6 @@
   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
@@ -108,9 +107,9 @@
   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} only for the same instance. Finally, the algorithm is 
+  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.
+  latter in term of energy and performance trade-off.}
 \end{abstract}
 
 
@@ -1148,7 +1147,7 @@ In section \ref{sec.grid5000}, since it was not possible to measure the static p
 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
@@ -1179,7 +1178,6 @@ In these experiments, the class D of the NAS parallel benchmarks are executed ov
   \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 
@@ -1198,17 +1196,17 @@ distance percentage is obtained with  the  10\% static power scenario  and this
 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
@@ -1220,7 +1218,9 @@ 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. 
+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}
@@ -1239,55 +1239,54 @@ presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-
   \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  MÃ©socentre de  calcul de  Franche-ComtÃ©. 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.
 
