X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/1f61e74e038befe7db3d062a959140ef6fb6badf..243f7b658f537226cfc68f4b1c5465c2c4cc094b:/paper.tex

diff --git a/paper.tex b/paper.tex
index 9ab93a9..bec8af6 100644
--- a/paper.tex
+++ b/paper.tex
@@ -67,7 +67,7 @@ kW). This large increase in number of computing cores has led to large energy
 consumption by these architectures. Moreover, the price of energy is expected to
 continue its ascent according to the demand. For all these reasons energy
 reduction became an important topic in the high performance computing field. To
-tackle this problem, many researchers used DVFS (Dynamic Voltage Frequency
+tackle this problem, many researchers used DVFS (Dynamic Voltage and Frequency
 Scaling) operations which reduce dynamically the frequency and voltage of cores
 and thus their energy consumption. However, this operation also degrades the
 performance of computation. Therefore researchers try to reduce the frequency to
@@ -101,7 +101,7 @@ algorithm gives better energy-time trade off.
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents the works
 from other authors.  Section~\ref{sec.exe} shows the execution of parallel
-tasks and sources of idle times.  It resumes the energy
+tasks and sources of idle times. Also, 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 trade offs
 objective function. Section~\ref{sec.optim} demonstrates the proposed
@@ -231,7 +231,7 @@ consumption~\cite{37}. The operational frequency \emph f depends linearly on the
 supply voltage $V$, i.e., $V = \beta \cdot f$ with some constant $\beta$. This
 equation is used to study the change of the dynamic voltage with respect to
 various frequency values in~\cite{3}. The reduction process of the frequency are
-expressed by the scaling factor \emph S. The scale \emph S is the ratio between the
+expressed by the scaling factor \emph S. This scaling factor is the ratio between the
 maximum and the new frequency as in EQ~(\ref{eq:s}).
 \begin{equation}
   \label{eq:s}
@@ -240,8 +240,8 @@ maximum and the new frequency as in EQ~(\ref{eq:s}).
 The value of the scale $S$ is greater than 1 when changing the frequency to any
 new frequency value~(\emph {P-state}) in governor, the CPU governor is an
 interface driver supplied by the operating system kernel (e.g. Linux) to
-lowering core's frequency.  The scaling factor is equal to 1 when the frequency
-set is to the maximum frequency.  The energy consumption model for parallel
+lowering core's frequency.  The scaling factor is equal to 1 when the new frequency is 
+set to the maximum frequency.  The energy consumption model for parallel
 homogeneous platform depends on the scaling factor \emph S. This factor reduces
 quadratically the dynamic power.  Also, this factor increases the static energy
 linearly because the execution time is increased~\cite{36}.  The energy model
@@ -310,7 +310,7 @@ these times are used to predict the execution time for any MPI program as a func
 the new scaling factor as in EQ~(\ref{eq:tnew}).
 \begin{equation}
   \label{eq:tnew}
- \textit  T_\textit{new} = T_\textit{Max Comp Old} \cdot S + T_{\textit{Max Comm Old}}
+ \textit  T_\textit{New} = T_\textit{Max Comp Old} \cdot S + T_{\textit{Max Comm Old}}
 \end{equation}
 The above equation shows that the scaling factor \emph S has linear relation
 with the computation time without affecting the communication time. The
@@ -362,7 +362,7 @@ simultaneously. But the main objective is to produce maximum energy reduction
 with minimum performance reduction. Many researchers used different strategies
 to solve this nonlinear problem for example see~\cite{19,42}, their methods add
 big overhead to the algorithm for selecting the suitable frequency. In this
-paper we are present a method to find the optimal scaling factor \emph S for
+paper we present a method to find the optimal scaling factor \emph S for
 optimize both energy and performance simultaneously without adding big
 overheads.  Our solution for this problem is to make the optimization process
 have the same direction. Therefore, we inverse the equation of normalize
@@ -381,7 +381,7 @@ performance as follows:
   \subfloat[Real Relation.]{%
     \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
   \label{fig:rel}
-  \caption{The Energy and Performance Relation}
+  \caption{The Relation of Energy and Performance }
 \end{figure*}
 Then, we can modelize our objective function as finding the maximum distance
 between the energy curve EQ~(\ref{eq:enorm}) and the inverse of performance
@@ -408,7 +408,7 @@ reasons that mentioned before.
 In the previous section we described the objective function that satisfy our
 goal in discovering optimal scaling factor for both performance and energy at
 the same time. Therefore, we develop an energy to performance scaling algorithm
-($EPSA$). This algorithm is simple and has a direct way to calculate the optimal
+(EPSA). This algorithm is simple and has a direct way to calculate the optimal
 scaling factor for both energy and performance at the same time.
 \begin{algorithm}[tp]
   \caption{EPSA}
@@ -454,14 +454,14 @@ in the MPI program.
   \caption{DVFS}
   \label{dvfs}
   \begin{algorithmic}[1]
- \For {$J:=1$ to $Some-Iterations \; $}
+ \For {$J=1$ to $Some-Iterations \; $}
   \State -Computations Section.
    \State -Communications Section.
    \If {$(J=1)$} 
      \State -Gather all times of computation and\par\hspace{13 pt} communication from each node.
      \State -Call EPSA with these times.
      \State -Calculate the new frequency from optimal scale.
-     \State -Set the new frequency to the system.
+     \State -Change the new frequency of the system.
    \EndIf
 \EndFor
 \end{algorithmic}
@@ -494,7 +494,7 @@ frequencies. We increased this range to verify the EPSA algorithm takes small ex
 time while it has a big number of available frequencies. The simulated network link is 1 GB Ethernet (TCP/IP). 
 The backbone of the cluster simulates a high performance switch.
 \begin{table}[htb]
-  \caption{Platform File Parameters}
+  \caption{SimGrid Platform File Parameters}
   % title of Table
   \centering
   \begin{tabular}{|*{7}{l|}}
@@ -530,7 +530,7 @@ 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
 and SP. 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. AS an
-example, we are present the execution times of the NAS benchmarks as in the
+example, we present the execution times of the NAS benchmarks as in the
 figure~(\ref{fig:pred}).
 
 \subsection{The EPSA Results}
@@ -569,7 +569,7 @@ same time over all available scales.
   \includegraphics[width=.33\textwidth]{lu.eps}\hfill%
   \includegraphics[width=.33\textwidth]{bt.eps}\hfill%
   \includegraphics[width=.33\textwidth]{ft.eps}
-  \caption{Optimal scaling factors for The NAS MPI Programs}
+  \caption{The Discovered  scaling factors for NAS MPI Programs}
   \label{fig:nas}
 \end{figure*}
 \begin{table}[htb]
@@ -737,7 +737,7 @@ NAS benchmarks programs for classes A,B and C.
 \end{table}
 As shown in these tables our scaling factor is not optimal for energy saving
 such as Rauber and Rünger scaling factor EQ~(\ref{eq:sopt}), but it is optimal for both
-the energy and the performance simultaneously. Our $EPSA$ optimal scaling factors
+the energy and the performance simultaneously. Our EPSA optimal scaling factors
 has better simultaneous optimization for both the energy and the performance
 compared to Rauber and Rünger energy-performance method ($R_{E-P}$). Also, in
 ($R_{E-P}$) method when setting the frequency to maximum value for the
@@ -761,12 +761,12 @@ than the first.
   \includegraphics[width=.33\textwidth]{compare_class_A.pdf}
   \includegraphics[width=.33\textwidth]{compare_class_B.pdf}
   \includegraphics[width=.33\textwidth]{compare_class_c.pdf}
-  \caption{Comparing Our EPSA with Rauber and Rünger Methods}
+  \caption {Comparing Our EPSA with Rauber and Rünger Methods}
   \label{fig:compare}
 \end{figure}
 \section{Conclusion}
 \label{sec.concl}
-In this paper we develop the simultaneous energy-performance algorithm. It works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value refer to more energy saving. Also, when the scaling factor is smaller value, Then it has bigger impact on performance than energy. The algorithm optimizes the energy saving and performance in the same time to have positive trade off. The optimal trade off represents the maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. In future, we will apply the EPSA algorithm on heterogeneous platform.
+In this paper we developed the simultaneous energy-performance algorithm. It works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value refer to more energy saving. Also, when the scaling factor is smaller value, then it has bigger impact on performance than energy. The algorithm optimizes the energy saving and performance in the same time to have positive trade off. The optimal trade off represents the maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. In future, we will apply the EPSA algorithm on heterogeneous platform.
 
 \section*{Acknowledgment}
 Computations have been performed on the supercomputer facilities of the