Use package url to format email addresses.

[mpi-energy.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index 9310147fbab6b4cde5f7e6477433a9318bf806ef..bcbfb5dc7ebbb8fd00740b2067f0bd7dd7edc4a1 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -10,6 +10,9 @@
 \usepackage{colortbl}
 \usepackage{amsmath}
 
 \usepackage{colortbl}
 \usepackage{amsmath}
 

+\usepackage{url}
+\DeclareUrlCommand\email{\urlstyle{same}}
+

 \usepackage[autolanguage,np]{numprint}
 \renewcommand*\npunitcommand[1]{\text{#1}}
 
 \usepackage[autolanguage,np]{numprint}
 \renewcommand*\npunitcommand[1]{\text{#1}}
 


@@ -32,25 +35,15 @@
   \IEEEauthorblockA{%
     FEMTO-ST Institute\\
     University of Franche-Comté\\
   \IEEEauthorblockA{%
     FEMTO-ST Institute\\
     University of Franche-Comté\\

-<<<<<<< HEAD
-    IUT de Belfort-Montb\'{e}liard, Rue Engel Gros, BP 27, 90016 Belfort, France\\
-   Fax  : (+33)~3~84~58~77~32\\
-   Email: \{jean-claude.charr, raphael.couturier, ahmed.fanfakh, arnaud.giersch\}@univ-fcomte.fr
-=======

     IUT de Belfort-Montbéliard, 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
     Fax  : +33~3~84~58~77~32\\
     IUT de Belfort-Montbéliard, 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
     Fax  : +33~3~84~58~77~32\\

-    Email: \{jean-claude.charr,raphael.couturier,ahmed.fanfakh\_badri\_muslim,arnaud.giersch\}@univ-fcomte.fr
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
+    Email: \email{{jean-claude.charr,raphael.couturier,ahmed.fanfakh_badri_muslim,arnaud.giersch}@univ-fcomte.fr}

    }
   }
 
 \maketitle
 
    }
   }
 
 \maketitle
 

-<<<<<<< HEAD
-=======

 \AG{Is the fax number correct? Shall we add a telephone number?}
 \AG{Is the fax number correct? Shall we add a telephone number?}

-\JC{Use Capital letters for only the first letter in the title of a section, table, figure, ...} 
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 \begin{abstract}
   Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. 
 This technique is usually used to reduce the energy consumed by a CPU while
 \begin{abstract}
   Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. 
 This technique is usually used to reduce the energy consumed by a CPU while


@@ -59,7 +52,7 @@ exponentially related to its frequency. Thus, decreasing the frequency reduces
 the power consumed by the CPU. However, it can also significantly affect the
 performance of the executed program if it is compute bound and a low CPU
 frequency is selected. The performance degradation ratio can even be higher than
 the power consumed by the CPU. However, it can also significantly affect the
 performance of the executed program if it is compute bound and a low CPU
 frequency is selected. The performance degradation ratio can even be higher than

-the saved energy ratio. Therefore, the chosen scaling factor must give the best possible tradeoff
+the saved energy ratio. Therefore, the chosen scaling factor must give the best possible trade-off

 between energy reduction and performance. 
 
 In this paper we present an algorithm
 between energy reduction and performance. 
 
 In this paper we present an algorithm


@@ -113,13 +106,13 @@ benchmarks (NPB v3.3) developed by NASA~\cite{44}. Our experiments are executed
 using the simulator SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183}
 over an homogeneous distributed memory architecture. Furthermore, we compare the
 proposed algorithm with Rauber and Rünger methods~\cite{3}.
 using the simulator SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183}
 over an homogeneous distributed memory architecture. Furthermore, we compare the
 proposed algorithm with Rauber and Rünger methods~\cite{3}.

-The comparison's results show that our algorithm gives better energy-time tradeoff.
+The comparison's results show that our algorithm gives better energy-time trade-off.

 
 This paper is organized as follows: Section~\ref{sec.relwork} presents related works
 from other authors.  Section~\ref{sec.exe} shows the execution of parallel
 tasks and sources of idle times.  It resumes the energy
 model of homogeneous platform. Section~\ref{sec.mpip} evaluates the performance
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents related works
 from other authors.  Section~\ref{sec.exe} shows the execution of parallel
 tasks and sources of idle times.  It resumes the energy
 model of homogeneous platform. Section~\ref{sec.mpip} evaluates the performance

-of MPI program.  Section~\ref{sec.compet} presents the energy-performance tradeoffs
+of MPI program.  Section~\ref{sec.compet} presents the energy-performance trade-offs

 objective function. Section~\ref{sec.optim} demonstrates the proposed energy-performance algorithm. Section~\ref{sec.expe} verifies the performance prediction
 model and presents the results of the proposed algorithm.  Also, It shows the comparison results. Finally,
 we conclude in Section~\ref{sec.concl}.
 objective function. Section~\ref{sec.optim} demonstrates the proposed energy-performance algorithm. Section~\ref{sec.expe} verifies the performance prediction
 model and presents the results of the proposed algorithm.  Also, It shows the comparison results. Finally,
 we conclude in Section~\ref{sec.concl}.


@@ -176,13 +169,8 @@ The primary contribution of this paper is presenting a new online scaling factor
 
 \section{Execution and energy of parallel tasks on homogeneous platform} 
 \label{sec.exe}
 
 \section{Execution and energy of parallel tasks on homogeneous platform} 
 \label{sec.exe}

-<<<<<<< HEAD
-%\AG{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'', can be deleted if we need space, we can just say we are interested in this paper in homogeneous clusters}
+%\JC{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'', can be deleted if we need space, we can just say we are interested in this paper in homogeneous clusters}

 \subsection{Parallel tasks execution on homogeneous platform}
 \subsection{Parallel tasks execution on homogeneous platform}

-=======
-\JC{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'', can be deleted if we need space, we can just say we are interested in this paper in homogeneous clusters}
-\subsection{Parallel Tasks Execution on Homogeneous Platform}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 A homogeneous cluster consists of identical nodes in terms of hardware and software. 
 Each node has its own memory and at least one processor which can
 be a multi-core. The nodes are connected via a high bandwidth network. Tasks
 A homogeneous cluster consists of identical nodes in terms of hardware and software. 
 Each node has its own memory and at least one processor which can
 be a multi-core. The nodes are connected via a high bandwidth network. Tasks


@@ -191,8 +179,8 @@ we consider execution of the synchronous tasks on distributed homogeneous
 platform. These tasks can exchange the data via synchronous message passing.
 \begin{figure*}[t]
   \centering
 platform. These tasks can exchange the data via synchronous message passing.
 \begin{figure*}[t]
   \centering

-  \subfloat[Sync. imbalanced communications]{\includegraphics[scale=0.67]{commtasks}\label{fig:h1}}
-  \subfloat[Sync. imbalanced computations]{\includegraphics[scale=0.67]{compt}\label{fig:h2}}
+  \subfloat[Sync. imbalanced communications]{\includegraphics[scale=0.67]{fig/commtasks}\label{fig:h1}}
+  \subfloat[Sync. imbalanced computations]{\includegraphics[scale=0.67]{fig/compt}\label{fig:h2}}

   \caption{Parallel tasks on homogeneous platform}
   \label{fig:homo}
 \end{figure*}
   \caption{Parallel tasks on homogeneous platform}
   \label{fig:homo}
 \end{figure*}


@@ -255,10 +243,6 @@ lower a core's frequency.  This factor reduces
 quadratically the dynamic power which may cause degradation in performance and thus, the increase of the static energy because the execution time is increased~\cite{36}. If the tasks are sorted according to their execution times before scaling in a descending order,   the total energy consumption model for a parallel
 homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as a function of   the scaling factor \emph S,   as in EQ~(\ref{eq:energy}).
 
 quadratically the dynamic power which may cause degradation in performance and thus, the increase of the static energy because the execution time is increased~\cite{36}. If the tasks are sorted according to their execution times before scaling in a descending order,   the total energy consumption model for a parallel
 homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as a function of   the scaling factor \emph S,   as in EQ~(\ref{eq:energy}).
 

-<<<<<<< HEAD
-=======
-\JC{Are you sure of the following equation}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 \begin{equation}
   \label{eq:energy}
   E = P_\textit{dyn} \cdot S_1^{-2} \cdot
 \begin{equation}
   \label{eq:energy}
   E = P_\textit{dyn} \cdot S_1^{-2} \cdot


@@ -268,29 +252,16 @@ homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as
 \end{equation}
 where \emph N is the number of parallel nodes,  $T_i  \  and  \  S_i \  for \  i=1,...,N$ are the execution times and scaling factors of the sorted tasks. Therefore,  $T1$ is the time of the slowest task,  and $S_1$ its scaling factor which should be the highest because they are proportional to
 the time values $T_i$.  The scaling factors are computed as in EQ~(\ref{eq:si}).
 \end{equation}
 where \emph N is the number of parallel nodes,  $T_i  \  and  \  S_i \  for \  i=1,...,N$ are the execution times and scaling factors of the sorted tasks. Therefore,  $T1$ is the time of the slowest task,  and $S_1$ its scaling factor which should be the highest because they are proportional to
 the time values $T_i$.  The scaling factors are computed as in EQ~(\ref{eq:si}).

-<<<<<<< HEAD
-=======
-\JC{This equation does not make sense either, what's S? there is no F}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 \begin{equation}
   \label{eq:si}
   S_i = S \cdot \frac{T_1}{T_i}
       = \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i}
 \end{equation}
 \begin{equation}
   \label{eq:si}
   S_i = S \cdot \frac{T_1}{T_i}
       = \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i}
 \end{equation}

-<<<<<<< HEAD

 In this paper we depend on
 In this paper we depend on

-=======
-\JC{The Rauber model was used for a parallel machine not a homogeneous platform}
-where $F$ is the number of available frequencies. In this paper we depend on
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 Rauber and Rünger energy model EQ~(\ref{eq:energy}) for two reasons: (1) this
 model is used for any number of concurrent tasks, and (2) we
 compare our algorithm with Rauber and Rünger scaling factor selection method which is based on
 EQ~(\ref{eq:energy}). The optimal scaling factor is computed by minimizing the derivation for this equation which produces EQ~(\ref{eq:sopt}).
 Rauber and Rünger energy model EQ~(\ref{eq:energy}) for two reasons: (1) this
 model is used for any number of concurrent tasks, and (2) we
 compare our algorithm with Rauber and Rünger scaling factor selection method which is based on
 EQ~(\ref{eq:energy}). The optimal scaling factor is computed by minimizing the derivation for this equation which produces EQ~(\ref{eq:sopt}).

-<<<<<<< HEAD
-=======
-\JC{what's the small n in the equation}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 
 \begin{equation}
   \label{eq:sopt}
 
 \begin{equation}
   \label{eq:sopt}


@@ -374,10 +345,10 @@ performance as follows:
 \begin{figure*}
   \centering
   \subfloat[Converted relation.]{%
 \begin{figure*}
   \centering
   \subfloat[Converted relation.]{%

-    \includegraphics[width=.4\textwidth]{file.eps}\label{fig:r1}}%
+    \includegraphics[width=.4\textwidth]{fig/file}\label{fig:r1}}%

   \qquad%
   \subfloat[Real relation.]{%
   \qquad%
   \subfloat[Real relation.]{%

-    \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
+    \includegraphics[width=.4\textwidth]{fig/file3}\label{fig:r2}}

   \label{fig:rel}
   \caption{The energy and performance relation}
 \end{figure*}
   \label{fig:rel}
   \caption{The energy and performance relation}
 \end{figure*}


@@ -500,10 +471,10 @@ time values. These scaling factors are computed by dividing the maximum
 frequency by the new one see EQ~(\ref{eq:s}). 
 \begin{figure*}[t]
   \centering
 frequency by the new one see EQ~(\ref{eq:s}). 
 \begin{figure*}[t]
   \centering

-  \includegraphics[width=.328\textwidth]{cg_per.eps}\hfill%
-  \includegraphics[width=.328\textwidth]{mg_pre.eps}\hfill%
- % \includegraphics[width=.4\textwidth]{bt_pre.eps}\qquad%
-  \includegraphics[width=.328\textwidth]{lu_pre.eps}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/cg_per}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/mg_pre}\hfill%
+ % \includegraphics[width=.4\textwidth]{fig/bt_pre}\qquad%
+  \includegraphics[width=.328\textwidth]{fig/lu_pre}\hfill%

   \caption{Comparing predicted to real execution time}
   \label{fig:pred}
 \end{figure*}
   \caption{Comparing predicted to real execution time}
   \label{fig:pred}
 \end{figure*}


@@ -511,15 +482,8 @@ frequency by the new one see EQ~(\ref{eq:s}).
 In our cluster there are 18 available frequency states for each processor. 
 This lead to 18 run states for each program. We use seven MPI programs of the
  NAS parallel benchmarks: CG, MG, EP, FT, BT, LU
 In our cluster there are 18 available frequency states for each processor. 
 This lead to 18 run states for each program. We use seven MPI programs of the
  NAS parallel benchmarks: CG, MG, EP, FT, BT, LU

-<<<<<<< HEAD

 and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The maximum normalized error between the predicted execution time and the real time (SimGrid time) for all programs is between 0.0073 to 0.031. The  better case is for CG and the worse case is for LU. 
 \subsection{The experimental results for the scaling algorithm }
 and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The maximum normalized error between the predicted execution time and the real time (SimGrid time) for all programs is between 0.0073 to 0.031. The  better case is for CG and the worse case is for LU. 
 \subsection{The experimental results for the scaling algorithm }

-=======
-and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The average normalized errors between the predicted execution time and
-the real time (SimGrid time) for all programs is between 0.0032 to 0.0133. 
-\JC{why compute the average error not the max}
-\subsection{The  experimental results}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 The proposed algorithm was applied to seven MPI programs of the NAS
 benchmarks (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and
 C). For each instance the benchmarks were executed on a number of processors
 The proposed algorithm was applied to seven MPI programs of the NAS
 benchmarks (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and
 C). For each instance the benchmarks were executed on a number of processors


@@ -548,12 +512,12 @@ energy saving percent and the minimum performance degradation percent at the
 same time from all available scaling factors.
 \begin{figure*}[t]
   \centering
 same time from all available scaling factors.
 \begin{figure*}[t]
   \centering

-  \includegraphics[width=.328\textwidth]{ep.eps}\hfill%
-  \includegraphics[width=.328\textwidth]{cg.eps}\hfill%
-  \includegraphics[width=.328\textwidth]{sp.eps}
-  \includegraphics[width=.328\textwidth]{lu.eps}\hfill%
-  \includegraphics[width=.328\textwidth]{bt.eps}\hfill%
-  \includegraphics[width=.328\textwidth]{ft.eps}
+  \includegraphics[width=.328\textwidth]{fig/ep}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/cg}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/sp}
+  \includegraphics[width=.328\textwidth]{fig/lu}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/bt}\hfill%
+  \includegraphics[width=.328\textwidth]{fig/ft}

   \caption{Optimal scaling factors for the predicted energy and performance of NAS benchmarks}
   \label{fig:nas}
 \end{figure*}
   \caption{Optimal scaling factors for the predicted energy and performance of NAS benchmarks}
   \label{fig:nas}
 \end{figure*}


@@ -592,12 +556,9 @@ optimal level without considering the performance as in EQ~(\ref{eq:sopt}). We
 refer to this scenario as $R_{E}$. The second scenario is similar to the first
 except setting the slower task to the maximum frequency (when the scale $S=1$)
 to keep the performance from degradation as mush as possible. We refer to this
 refer to this scenario as $R_{E}$. The second scenario is similar to the first
 except setting the slower task to the maximum frequency (when the scale $S=1$)
 to keep the performance from degradation as mush as possible. We refer to this

-<<<<<<< HEAD
-scenario as $R_{E-P}$. While we refer to our algorithm as EPSA. The comparison is made in tables~(\ref{table:compareA},\ref{table:compareB},\ref{table:compareC}). These
-=======
-scenario as $R_{E-P}$. The comparison is made in tables~(\ref{table:compareA},
-\ref{table:compareB}, and \ref{table:compareC}). These
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
+scenario as $R_{E-P}$. While we refer to our algorithm as EPSA. The comparison
+is made in tables \ref{table:compareA}, \ref{table:compareB},
+and~\ref{table:compareC}. These

 tables show the results of our method and Rauber and Rünger scenarios for all the
 NAS benchmarks programs for classes A,B and C.
 \begin{table}[p]
 tables show the results of our method and Rauber and Rünger scenarios for all the
 NAS benchmarks programs for classes A,B and C.
 \begin{table}[p]


@@ -728,34 +689,31 @@ As shown in tables~\ref{table:compareA},~\ref{table:compareB} and~\ref{table:com
 
 Figure~(\ref{fig:compare}) shows the maximum distance between the energy saving
 percent and the performance degradation percent.  
 
 Figure~(\ref{fig:compare}) shows the maximum distance between the energy saving
 percent and the performance degradation percent.  

-Negative values mean that one of the two objectives (energy or performance) have been degraded more than the other. The positive tradeoffs with the highest values lead to maximum energy savings
+Negative values mean that one of the two objectives (energy or performance) have been degraded more than the other. The positive trade-offs with the highest values lead to maximum energy savings

 while keeping the performance degradation as low as possible. Our algorithm always
 while keeping the performance degradation as low as possible. Our algorithm always

-gives the highest positive energy to performance tradeoffs while Rauber and Rünger method
-($R_{E-P}$) gives in some time negative tradeoffs such as in BT and
+gives the highest positive energy to performance trade-offs while Rauber and Rünger method
+($R_{E-P}$) gives in some time negative trade-offs such as in BT and

 EP.
 \begin{figure*}[t]
   \centering
 EP.
 \begin{figure*}[t]
   \centering

-  \includegraphics[width=.328\textwidth]{compare_class_A.pdf}
-  \includegraphics[width=.328\textwidth]{compare_class_B.pdf}
-  \includegraphics[width=.328\textwidth]{compare_class_c.pdf}
+  \includegraphics[width=.328\textwidth]{fig/compare_class_A}
+  \includegraphics[width=.328\textwidth]{fig/compare_class_B}
+  \includegraphics[width=.328\textwidth]{fig/compare_class_C}

   \caption{Comparing our method to Rauber and Rünger methods}
   \label{fig:compare}
 \end{figure*}
 \section{Conclusion}
 \label{sec.concl}
   \caption{Comparing our method to Rauber and Rünger methods}
   \label{fig:compare}
 \end{figure*}
 \section{Conclusion}
 \label{sec.concl}

-In this paper, we have presented a new online scaling factor selection method that optimizes simultaneously the energy and performance of a distributed application running on an homogeneous cluster. It uses the computation and communication times measured at the first iteration to predict energy consumption and the performance of the parallel application at every available frequency. Then, it selects the scaling factor that gives the best tradeoff between energy reduction and performance which is the maximum distance between the energy and the inverted performance curves. To evaluate this method, we have applied it to the NAS benchmarks and it was compared to Rauber and Rünger methods while being executed on the simulator SimGrid. The results showed that our method, outperforms Rauber and Rünger methods in terms of energy-performance ratio. 
+In this paper, we have presented a new online scaling factor selection method that optimizes simultaneously the energy and performance of a distributed application running on an homogeneous cluster. It uses the computation and communication times measured at the first iteration to predict energy consumption and the performance of the parallel application at every available frequency. Then, it selects the scaling factor that gives the best trade-off between energy reduction and performance which is the maximum distance between the energy and the inverted performance curves. To evaluate this method, we have applied it to the NAS benchmarks and it was compared to Rauber and Rünger methods while being executed on the simulator SimGrid. The results showed that our method, outperforms Rauber and Rünger methods in terms of energy-performance ratio. 

 
 In the near future, we would like to adapt this scaling factor selection method to heterogeneous platforms where each node has different characteristics. In particular, each CPU has different available frequencies, energy consumption and performance. It would be also interesting to develop a new energy model for asynchronous parallel iterative methods where the number of iterations is not known in advance and depends on the global convergence of the iterative system.
 
 
 \section*{Acknowledgment}
 
 In the near future, we would like to adapt this scaling factor selection method to heterogeneous platforms where each node has different characteristics. In particular, each CPU has different available frequencies, energy consumption and performance. It would be also interesting to develop a new energy model for asynchronous parallel iterative methods where the number of iterations is not known in advance and depends on the global convergence of the iterative system.
 
 
 \section*{Acknowledgment}

+\AG{Jean-Claude, why did you remove the Mésocentre here?}

 As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 
 As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 

-<<<<<<< HEAD
-=======
-\JC{delete the online paths for each reference\AG{except for TOP500 and the NPB}}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b

 % trigger a \newpage just before the given reference
 % number - used to balance the columns on the last page
 % adjust value as needed - may need to be readjusted if
 % trigger a \newpage just before the given reference
 % number - used to balance the columns on the last page
 % adjust value as needed - may need to be readjusted if


@@ -774,4 +732,4 @@ Babylon (Iraq) for supporting his work.
 %%% End:
 
 %  LocalWords:  Fanfakh Charr FIXME Tianhe DVFS HPC NAS NPB SMPI Rauber's Rauber
 %%% End:
 
 %  LocalWords:  Fanfakh Charr FIXME Tianhe DVFS HPC NAS NPB SMPI Rauber's Rauber

-%  LocalWords:  CMOS EQ EPSA Franche Comté Tflop Rünger
+%  LocalWords:  CMOS EQ EPSA Franche Comté Tflop Rünger IUT Maréchal Juin cedex