corrected final section

[mpi-energy2.git] / mpi-energy2-extension / Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 296c5d6eed56d468778be978fcd57e07053f1c50..f00d0654ef85249aa932af30f4d08dea4dea937d 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -85,6 +85,34 @@
 
 \maketitle
 
 
 \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}{
 \section{Introduction}
 \label{sec.intro}
 \textcolor{blue}{


@@ -1119,7 +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.
 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
 
  \begin{figure}
   \centering


@@ -1150,7 +1178,6 @@ In these experiments, the class D of the NAS parallel benchmarks are executed ov
   \label{fig:fre-pow}
 \end{figure}
 
   \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 
 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 


@@ -1169,11 +1196,11 @@ 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 
 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 
 \label{sec.compare_EDP}
 
 Finding the frequencies that gives the best tradeoff between the energy consumption and the performance for a parallel 


@@ -1191,7 +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 
 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}
 \begin{figure}
   \centering
   \includegraphics[scale=0.5]{fig/edp_eng}


@@ -1210,20 +1239,20 @@ 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}
   \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.
 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.
 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 
 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}
 
 
 \section{Conclusion}