Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/mpi-energy2

[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 9592bffa8ec0ea493945c8240554cb33ef08ffdf..296c5d6eed56d468778be978fcd57e07053f1c50 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -97,7 +97,7 @@ 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{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
 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 +107,7 @@ This heterogeneous platform executes more than 7 GFLOPS per watt while consuming
 50.32 kilowatts.
 }
 
 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
 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 +121,14 @@ 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
 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,
 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
 
 \textcolor{blue}{
 This paper is organized as follows: Section~\ref{sec.relwork} presents some


@@ -443,12 +446,15 @@ 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
 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
 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.
 
 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 +1043,7 @@ to 10\% and are higher than those executed over the one site multi-cores scenari
 which on average is equal to 7\%. 
 
 \textcolor{blue}{
 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.}
 
 
 frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.}