X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/db7215cdaa5f7053e2332a73837622710a03ee23..7f9ca96425114b39b06dc8c85ff608e87facf26f:/mpi-energy2-extension/Heter_paper.tex?ds=sidebyside

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 9592bff..f00d065 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -85,6 +85,34 @@
 
 \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}{
@@ -97,7 +125,7 @@ Tianhe-2 had the highest FLOPS in June 2015  according to the Top500 list
 \cite{TOP500_Supercomputers_Sites}.  However, it was also the most power hungry
 platform with its over 3 million cores consuming around 17.8 megawatts.
 Moreover, according to the U.S.  annual energy outlook 2015
-\cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour
+\cite{U.S_Annual.Energy.Outlook.2015}, the price of energy for 1 megawatt-hour
 was approximately equal to \$70.  Therefore, the price of the energy consumed by
 the Tianhe-2 platform is approximately more than \$10 million each year.  The
 computing platforms must be more energy efficient and offer the highest number
@@ -107,6 +135,7 @@ 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
@@ -120,12 +149,14 @@ trade-off between the energy reduction and performance degradation ratio. In
 the energy consumption of message passing iterative applications running over
 homogeneous  and heterogeneous clusters respectively.  
 The results of the experiments show significant energy
-consumption reductions. In this paper, a new frequency selecting algorithm
-adapted for heterogeneous platform is presented. It selects the vector of
+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.
+works online and does not need any training or profiling.}
 
 \textcolor{blue}{
 This paper is organized as follows: Section~\ref{sec.relwork} presents some
@@ -443,12 +474,15 @@ appropriate frequency scaling factor for each processor while considering the
 characteristics of each processor (computation power, range of frequencies,
 dynamic and static powers) and the task executed (computation/communication
 ratio). The aim being to reduce the overall energy consumption and to avoid
-increasing significantly the execution time.  In our previous
-work~\cite{Our_first_paper,pdsec2015}, we proposed a method that selects the optimal
-frequency scaling factor for a homogeneous and heterogeneous clusters executing a message passing
+increasing significantly the execution time.
+\textcolor{blue}{  In our previous
+works~\cite{Our_first_paper} and \cite{pdsec2015}, we proposed a methods that select the optimal
+frequency scaling factors for a homogeneous and a heterogeneous clusters respectively. 
+Both of the two methods executing a message passing
 iterative synchronous application while giving the best trade-off between the
 energy consumption and the performance for such applications.  In this work we
-are interested in heterogeneous grid as described above.  Due to the
+are interested in heterogeneous grid as described above.}
+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.
 
@@ -1037,7 +1071,7 @@ to 10\% and are higher than those executed over the one site multi-cores scenari
 which on average is equal to 7\%. 
 
 \textcolor{blue}{
-The performance degradation percentages over one site multi-cores is lower because  the computations to communications ratio is decreased. Therefore, selecting small 
+The performance degradation percentages over one site multi-cores is lower because  the computations to communications ratio is decreased. Therefore, selecting bigger 
 frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.}
 
 
@@ -1113,7 +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
@@ -1144,7 +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 
@@ -1163,11 +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 
-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 
@@ -1185,7 +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}
@@ -1204,20 +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}
-\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}
