X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/bca40222c180bebb0a53ee94137105f59eed605d..36c95dc5298ac5cb8eaaae777eb92ac82c77d7cb:/mpi-energy2-extension/Heter_paper.tex?ds=sidebyside

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 5f869cb..6754e6f 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -107,9 +107,9 @@
 
 
 
-\title{Energy Consumption Reduction with DVFS for Message \\
+\title{Optimizing Energy Consumption with DVFS for Message \\
          Passing Iterative Applications on \\
-                    Grid Architecture} 
+                    Grid Architectures} 
   
 
 
@@ -200,17 +200,14 @@ the number of FLOPS executed by the processor which may increase the execution
 time of the application running over that processor.  Therefore, researchers use
 different optimization strategies to select the frequency that gives the best
 trade-off between the energy reduction and performance degradation ratio. In
-\cite{Our_first_paper} and \cite{pdsec2015} , a frequency selecting algorithm
+\cite{Our_first_paper} and \cite{pdsec2015}, a frequency selecting algorithm
 was proposed to reduce the energy consumption of message passing iterative
 applications running over homogeneous and heterogeneous clusters respectively.
 The results of the experiments showed significant energy consumption
 reductions. All the experimental results were conducted over the SimGrid
-simulator \cite{SimGrid}, which offers easy tools to create homogeneous and
-heterogeneous platforms and runs message passing parallel applications over
-them. %
-\AG{[\dots], which offers easy tools to describe homogeneous and heterogeneous
-  platforms, and to simulate the execution of message passing parallel
-  applications over them.}%
+simulator \cite{SimGrid}, which offers easy tools to describe homogeneous and heterogeneous  platforms, and to simulate the execution of message passing parallel
+applications over them. 
+
 In this paper, a new frequency selecting algorithm, adapted to grid platforms
 composed of heterogeneous clusters, is presented. It is applied to the NAS
 parallel benchmarks and evaluated over a real testbed, the Grid'5000 platform
@@ -234,6 +231,7 @@ NAS parallel benchmarks and executing them on the Grid'5000 testbed.
 It also evaluates the algorithm over multi-cores per node architectures and over three different power scenarios. Moreover, it shows the
 comparison results between the proposed method and an existing method.  Finally,
 in Section~\ref{sec.concl} the paper ends with a summary and some future works.
+
 \section{Related works}
 \label{sec.relwork}
 
@@ -394,7 +392,7 @@ and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ dur
 first  iteration.  The execution time for one iteration is equal to the sum of the maximum computation time for all nodes with the new scaling factors
 and the slowest communication time without slack time during one iteration.
 The latter is equal to the  communication time of the slowest node in the slowest cluster $h$.
-It means\AG[]{It means that\dots} only the communication time without any slack time is taken into account.
+It means that only the communication time without any slack time is taken into account.
 Therefore, the execution time of the iterative application is equal to
 the execution time of one iteration as in (\ref{eq:perf}) multiplied by the
 number of iterations of that application.
@@ -492,9 +490,9 @@ In the considered heterogeneous grid platform, each node $j$ in cluster $i$ may
 different dynamic and static powers from the nodes of the other clusters, 
 noted as $\Pd[ij]$ and $\Ps[ij]$ respectively.  Therefore, even if the distributed 
 message passing iterative application is load balanced, the computation time of each CPU $j$ 
-in cluster $i$ noted $\Tcp[ij]$ may be different and different frequency scaling factors may be
+in cluster $i$ noted $\Tcp[ij]$ may be slightly different due to the delay caused by the scheduler of the operating system. Therefore, different frequency scaling factors may be
 computed in order to decrease the overall energy consumption of the application
-and reduce slack times.  The communication time of a processor $j$ in cluster $i$ is noted as
+and reduce the slack times.  The communication time of a processor $j$ in cluster $i$ is noted as
 $\Tcm[ij]$ and could contain slack times when communicating with slower nodes,
 see Figure~\ref{fig:heter}.  Therefore, all nodes do not have equal
 communication times. While the dynamic energy is computed according to the
@@ -543,9 +541,10 @@ frequency scaling factors for a homogeneous and a heterogeneous cluster respecti
 Both methods selects the frequencies that gives the best trade-off between 
 energy consumption reduction and performance for  message passing
 iterative synchronous applications.   In this work we
-are interested in grids that are composed of heterogeneous clusters were the nodes have different characteristics such  as  dynamic power, static power, computation power, frequencies range, network latency and bandwidth. 
-Due to the
-heterogeneity of the processors, a vector of scaling factors should be selected
+are interested in grids that are composed of heterogeneous clusters were the nodes 
+have different characteristics such  as  dynamic power, static power, computation power, 
+frequencies range, network latency and bandwidth. 
+Due to the heterogeneity of the processors, a vector of scaling factors should be selected
 and it must give the best trade-off between energy consumption and performance.
 
 The relation between the energy consumption and the execution time for an
@@ -773,9 +772,7 @@ Therefore, the algorithm iterates on all remaining frequencies, from the higher
 bound until all nodes reach their minimum frequencies or their lower bounds, to compute the overall
 energy consumption and performance and selects the optimal vector of the frequency scaling
 factors. At each iteration the algorithm determines the slowest node
-according to Equation~\ref{eq:perf}
-%\AG[]{Be consistent: remove word ``Equation'' and add parentheses around equation number, here and all along the rest of the text.}
-and keeps its frequency unchanged,
+according to Equation~\ref{eq:perf} and keeps its frequency unchanged,
 while it lowers the frequency of all other nodes by one gear.  The new overall
 energy consumption and execution time are computed according to the new scaling
 factors.  The optimal set of frequency scaling factors is the set that gives the
@@ -789,11 +786,7 @@ factor should start from the maximum frequency because the performance and the
 consumed energy decrease from the beginning of the plot. On the other hand, in
 the  grid platform the performance is maintained at the beginning of the
 plot even if the frequencies of the faster nodes decrease until the computing
-power of scaled down nodes are lower than the slowest node. In other words,
-\AG[]{That's not a sentence.}
-until they reach the higher bound. It can also be noticed that the higher the
-difference between the faster nodes and the slower nodes is, the bigger the
-maximum distance between the energy curve and the performance curve is, which results in bigger energy savings. 
+power of scaled down nodes are lower than the slowest node. It can also be noticed that the higher the difference between the faster nodes and the slower nodes is, the bigger the maximum distance between the energy curve and the performance curve is, which results in bigger energy savings. 
 
 
 \section{Experimental results}
@@ -851,8 +844,6 @@ selected clusters and are presented in Table~\ref{table:grid5000}.
 \begin{figure}[!t]
   \centering
   \includegraphics[scale=0.6]{fig/power_consumption.pdf}
-  \AG{I don't understand the labels on the horizontal axis: 10:30:37, 10:30:38,
-    etc.}
   \caption{The power consumption by one core from the Taurus cluster}
   \label{fig:power_cons}
 \end{figure}
@@ -970,17 +961,16 @@ The long distance communications between the two distributed sites increase the
 
 The execution times of these benchmarks 
 over one site with 16 and 32 nodes are also lower when  compared to those of the  two sites 
-scenario. Moreover, most of the benchmarks running over the one site scenario their execution times  are approximately divided by two  when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes).\AG{Parse error (cannot understand the previous sentence).}
+scenario. Moreover, most of the benchmarks running over the one site scenario have their execution times  approximately divided by two  when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes).
 
 However, the execution times and the energy consumptions of EP and MG
 benchmarks, which have no or small communications, are not significantly
 affected in both scenarios, even when the number of nodes is doubled.  On the
-other hand, the communications\AG[]{the communication time?} of the rest of the benchmarks increases when
+other hand, the communication times of the rest of the benchmarks increases when
 using long distance communications between two sites or increasing the number of
 computing nodes.
 
 
-
 The energy saving percentage is computed as the ratio between the reduced 
 energy consumption, Equation~\ref{eq:energy}, and the original energy consumption,
 Equation~\ref{eq:eorginal}, for all benchmarks as in Figure~\ref{fig:eng_s}. 
