-\title{Energy Consumption Reduction with DVFS for Message \\
+\title{Optimizing Energy Consumption with DVFS for Message \\
Passing Iterative Applications on \\
- Grid Architecture}
+ Grid Architectures}
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
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}
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.
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
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
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}
\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}
\begin{tabular}{|*{7}{c|}}
\hline
& & Max & Min & Diff. & & \\
- Cluster & CPU & Freq. & Freq. & Freq. & No. of cores & Dynamic power \\
+ Cluster & CPU & Freq. & Freq. & Freq. & Cores & Dynamic power \\
Name & model & GHz & GHz & GHz & per CPU & of one core \\
\hline
& Intel & & & & & \\
\begin{tabular}{|*{4}{c|}}
\hline
\multirow{2}{*}{Scenario name} & \multicolumn{3}{c|} {The participating clusters} \\ \cline{2-4}
- & Cluster & Site & No. of nodes \\
+ & Cluster & Site & Nodes per cluster \\
\hline
\multirow{3}{*}{Two sites / 16 nodes} & Taurus & Lyon & 5 \\ \cline{2-4}
& Graphene & Nancy & 5 \\ \cline{2-4}
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}.
\caption{The multicores scenarios}
\begin{tabular}{|*{4}{c|}}
\hline
-Scenario name & Cluster name & \begin{tabular}[c]{@{}c@{}}No. of nodes\\ in each cluster\end{tabular} &
- \begin{tabular}[c]{@{}c@{}}No. of cores\\ for each node\end{tabular} \\ \hline
+Scenario name & Cluster name & Nodes per cluster &
+ Cores per node \\ \hline
\multirow{3}{*}{One core per node} & Graphite & 4 & 1 \\ \cline{2-4}
& Graphene & 14 & 1 \\ \cline{2-4}
& Griffon & 14 & 1 \\ \hline
