contribution

[mpi-energy.git] / paper.tex

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@@ -156,7 +156,7 @@ To maintain the performance of the parallel program , they
 set the  processor with the biggest load to the highest gear and then compute the scaling  factor values for the rest of the processors. Although this model was built for parallel architectures, it can be adapted  to distributed architectures by taking into account the communications. 
 The primary contribution of our paper is presenting a new online scaling factor selection method which has the following characteristics :
 \begin{enumerate}
 set the  processor with the biggest load to the highest gear and then compute the scaling  factor values for the rest of the processors. Although this model was built for parallel architectures, it can be adapted  to distributed architectures by taking into account the communications. 
 The primary contribution of our paper is presenting a new online scaling factor selection method which has the following characteristics :
 \begin{enumerate}

-\item It is based on Rauber and Rünger analytical model to predict the energy consumption and the execution time of the application with different frequency gears. 
+\item It is based on Rauber and Rünger analytical model to predict the energy consumption  of the application with different frequency gears. 

 \item It selects the frequency scaling factor for simultaneously optimizing energy reduction and maintaining performance.
 \item It is well adapted to distributed architectures because it takes into account the communication time.
 \item It is well adapted to distributed applications with imbalanced tasks.
 \item It selects the frequency scaling factor for simultaneously optimizing energy reduction and maintaining performance.
 \item It is well adapted to distributed architectures because it takes into account the communication time.
 \item It is well adapted to distributed applications with imbalanced tasks.