Typos.

[mpi-energy.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index e3efb3257951e5b69b9b5005508b67e1df4dcbe3..5ba8cc7b592dbd5c46aab3b6dfc2931b806e3b1c 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -49,7 +49,7 @@ 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


@@ -103,13 +103,13 @@ 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}.


@@ -686,10 +686,10 @@ 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


@@ -701,7 +701,7 @@ EP.
 \end{figure*}
 \section{Conclusion}
 \label{sec.concl}
 \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.
 
 
 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.
 


@@ -729,4 +729,4 @@ 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