Reword first 3 lines of the second paragraph of the abstract.

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 0203c6796a05ca332f15f44a95e69268b562055a..af3d66087651d7dc6e5cc3fa2f42af3d3dc0442a 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -88,9 +88,10 @@ platforms many techniques have been  used. Dynamic voltage and frequency scaling
 consumption.  However,  lowering the  frequency  of  a  CPU might  increase  the
 execution  time of  an application  running on  that processor.   Therefore, the
 frequency that  gives the best trade-off  between the energy  consumption and the
 consumption.  However,  lowering the  frequency  of  a  CPU might  increase  the
 execution  time of  an application  running on  that processor.   Therefore, the
 frequency that  gives the best trade-off  between the energy  consumption and the

-performance of an application must be selected.\\
-In this  paper, a new  online frequencies selecting algorithm  for heterogeneous
-platforms is presented.   It selects the frequency which tries  to give the best
+performance of an application must be selected.
+
+In this  paper, a new  online frequency selecting algorithm  for heterogeneous
+platforms is presented.   It selects the frequencies and tries  to give the best

 trade-off  between  energy saving  and  performance  degradation,  for each  node
 computing the message  passing iterative application. The algorithm  has a small
 overhead and works without training or profiling. It uses a new energy model for
 trade-off  between  energy saving  and  performance  degradation,  for each  node
 computing the message  passing iterative application. The algorithm  has a small
 overhead and works without training or profiling. It uses a new energy model for


@@ -714,7 +715,7 @@ brute force algorithm. It has a small execution time: for a heterogeneous
 cluster composed of four different types of nodes having the characteristics
 presented in Table~\ref{table:platform}, it takes on average \np[ms]{0.04} for 4
 nodes and \np[ms]{0.15} on average for 144 nodes to compute the best scaling
 cluster composed of four different types of nodes having the characteristics
 presented in Table~\ref{table:platform}, it takes on average \np[ms]{0.04} for 4
 nodes and \np[ms]{0.15} on average for 144 nodes to compute the best scaling

-factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$
+factors vector.  The algorithm complexity is $O(F\cdot N)$, where $F$

 is the number of iterations and $N$ is the number of computing nodes. The
 algorithm needs from 12 to 20 iterations to select the best vector of frequency
 scaling factors that gives the results of the next sections.
 is the number of iterations and $N$ is the number of computing nodes. The
 algorithm needs from 12 to 20 iterations to select the best vector of frequency
 scaling factors that gives the results of the next sections.


@@ -748,22 +749,22 @@ nodes were connected via an Ethernet network with 1 Gbit/s bandwidth.
   \caption{Heterogeneous nodes characteristics}
   % title of Table
   \centering
   \caption{Heterogeneous nodes characteristics}
   % title of Table
   \centering

-  \begin{tabular}{|*{7}{l|}}
+  \begin{tabular}{|*{7}{r|}}

     \hline
     Node          &Simulated  & Max      & Min          & Diff.          & Dynamic      & Static \\
     type          &GFLOPS     & Freq.    & Freq.        & Freq.          & power        & power \\
                   &           & GHz      & GHz          &GHz             &              &       \\
     \hline
     \hline
     Node          &Simulated  & Max      & Min          & Diff.          & Dynamic      & Static \\
     type          &GFLOPS     & Freq.    & Freq.        & Freq.          & power        & power \\
                   &           & GHz      & GHz          &GHz             &              &       \\
     \hline

-    1             &40         & 2.5      & 1.2          & 0.1            & 20~W         &4~W    \\
+    1             &40         & 2.50     & 1.20         & 0.100          & \np[W]{20}   &\np[W]{4} \\

          
     \hline
          
     \hline

-    2             &50         & 2.66     & 1.6          & 0.133          & 25~W         &5~W    \\
+    2             &50         & 2.66     & 1.60         & 0.133          & \np[W]{25}   &\np[W]{5} \\

                   
     \hline
                   
     \hline

-    3             &60         & 2.9      & 1.2          & 0.1            & 30~W         &6~W    \\
+    3             &60         & 2.90     & 1.20         & 0.100          & \np[W]{30}   &\np[W]{6} \\

                   
     \hline
                   
     \hline

-    4             &70         & 3.4      & 1.6          & 0.133          & 35~W         &7~W    \\
+    4             &70         & 3.40     & 1.60         & 0.133          & \np[W]{35}   &\np[W]{7} \\

                   
     \hline
   \end{tabular}
                   
     \hline
   \end{tabular}