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\todo[color=red!10,#1]{\sffamily\textbf{JC:} #2}\xspace}
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\section{\small {Introduction and Problem definition}}
\bf \textcolor{blue}{Approaches to increase the computing power:}
\begin{minipage}{0.5\textwidth}
- \textcolor{blue}{1)} \small \bf \textcolor{black}{Increasing the frequency of processor}
+ \textcolor{blue}{1)} \small \bf \textcolor{black}{Increasing the frequency of a processor}
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-%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 04 %%
-%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Introduction and problem definition}
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- \textcolor{blue}{$\blacktriangleright$}
- \small \bf \textcolor{black}{ The power consumption of a processor increases exponentially when its
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-
- \begin{minipage}{0.5\textwidth}
- \textcolor{blue}{$\blacktriangleright$}
- \small \bf \textcolor{black}{The biggest power consumption is consumed by a processor in the computing node}
-
- \end{minipage}%
- \begin{minipage}{0.6\textwidth}
- \begin{figure}[h!]
- \includegraphics[width=0.9\textwidth]{fig/node-power}
- \end{figure}
- \end{minipage}%
-
- \end{frame}
-
+
%%%%%%%%%%%%%%%%%%%
-%% SLIDE 05 %%
+%% SLIDE 04 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Introduction and problem definition}
\vspace{0.1cm}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 07 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Using the energy reduction method}
-\section{\small {Using the energy reduction method}}
-\begin{block}{\textcolor{white}{Why we used DVFS method:}}
-\begin{itemize}
- \item \textcolor{black}{It used to reduce the energy while keeping all node working, thus it is more conventional with parallel computing.}
- \item \textcolor{black}{It has a very small overhead compared to switch-off idle nodes method.}
+\begin{frame}{Motivations}
+\vspace{0.05cm}
+\section{\small {Motivations}}
+\textcolor{blue}{Why we used DVFS method:}
+\vspace{-0.49cm}
+\begin{minipage}{0.5\textwidth}
+ \vspace{-0.49cm}
+ \begin{itemize}
+ \item \small \textcolor{black}{The biggest power consumption is consumed by a processor \textsuperscript{1}. }
+
\end{itemize}
-\end{block}
- \vspace{0.1cm}
+ \end{minipage}%
+ \begin{minipage}{0.5\textwidth}
+ \vspace{-0.49cm}
+ \begin{figure}[h!]
+ \includegraphics[width=0.85\textwidth]{fig/node-power}
+
+ \end{figure}
+ \end{minipage}%
+
+ \begin{itemize} \item \small \textcolor{black}{It used to reduce the energy consumption while keeping all the node working, thus it is more adapted to parallel computing.}
+ \item \small \textcolor{black}{It has a very small overhead compared to switching-off the idle nodes method.} \end{itemize}
+
+\vspace{-0.12cm}
+
\begin{block}{\textcolor{white}{Challenge and Objective}}
- \textcolor{blue}{Challenge:} \textcolor{black}{DVFS is used to reduce the energy, \textcolor{blue}{but} it degrades the performance simultaneously.}
+ \small \textcolor{blue}{Challenge:} \textcolor{black}{DVFS is used to reduce the energy consumption, \textcolor{blue}{but} it degrades the performance simultaneously.}
\vspace{0.1cm}
- \textcolor{blue}{Objective:} \textcolor{black}{Optimizing both energy consumption and performance of a parallel application at the same time when DVFS is used.}
+ \small \textcolor{blue}{Objective:} \textcolor{black}{Applying the DVFS to minimize the energy consumption while maintaining the performance of the parallel applications.}
\end{block}
+
+ \tiny \textsuperscript{1} Fan, X., Weber, W., and Barroso, L. A. 2007. Power provisioning
+for a warehouse-sized computer.
\end{frame}
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Contributions}
-\section{\small {Contributions}}
-\subsection{\small {3.1 Energy optimization of homogeneous platform}}
+\begin{frame}{Contribution}
+
+\section{\small {Energy optimization of homogeneous platform}}
\begin{center}
-\bf \textcolor{black}{First contribution} \\
-\vspace{1cm}
-\bf \Large \textcolor{blue}{Energy optimization of homogeneous platform}
+\bf \Large \textcolor{blue}{Energy optimization of a parallel application with iterations running over a homogeneous platform}
\end{center}
\end{frame}
\begin{frame}{Objectives}
\begin{femtoBlock}{} \vspace{-12 mm}
\begin{itemize} \small
- \item Study the effect of the scaling factor $S$ on \textbf{energy consumption} of parallel iterative applications such as NAS
+ \item Study the effect of the scaling factor $S$ on \textbf{energy consumption and performance } of parallel applications with iterations such as NAS
Benchmarks. \includegraphics[width=.06\textwidth]{c1/nasa.pdf} \medskip
- \item Study the effect of the scaling factor $S$ on \textbf{performance} of these benchmarks.\medskip
- \item Discovering the \textbf{energy-performance trade-off relation} when changing the frequency.\medskip
- \item We propose an algorithm for selecting the scaling factor $S$ producing \textbf {optimal trade-off} between the energy and performance. \medskip
- \item Improving Rauber and Rünger's\footnote{\tiny Thomas Rauber and Gudula Rünger. Analytical modeling and simulation of the
- energy consumption \\ \quad ~ ~\quad of independent tasks. In Proceedings of the Winter Simulation Conference, 2012.} method that our method best on.
+
+ \item Discovering the \textbf{energy-performance trade-off relation} when changing the frequency of the processor.\medskip
+ \item Proposing an algorithm for selecting the scaling factor $S$ producing \textbf {the optimal trade-off} between the energy consumption and the performance. \medskip
+ \item Comparing the proposed algorithm to existing methods.
+
+
+ %\footnote{\tiny Thomas Rauber and Gudula Rünger. Analytical modeling and simulation of the
+ %energy consumption \\ \quad ~ ~\quad of independent tasks. In Proceedings of the Winter Simulation Conference, 2012.} method that our method best on.
\end{itemize}
- \let\thefootnote\relax\footnote{}
+ %\let\thefootnote\relax\footnote{}
\vspace{-10 mm}
\end{femtoBlock}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Parallel tasks execution over Homo. Platform}
+\begin{frame}{Execution of synchronous parallel tasks}
\vspace{-0.5 cm}
\begin{figure}
\centering
\includegraphics[scale=0.49]{c1/commtasks}\label{fig:h1}}
\subfloat[Sync. imbalanced computations]{%
\includegraphics[scale=0.49]{c1/compt}\label{fig:h2}}
- \caption{Parallel tasks on homogeneous platform}
+ % \caption{Parallel tasks on homogeneous platform}
\label{fig:homo}
\end{figure}
%\vspace{-0.3cm}
\small
\begin{block}{\small Our objective function}
- \centering{$\textbf{\emph {MaxDist}} = \max_{j=1,2,\dots ,F}
- (\overbrace{P_{Norm}(S_j)}^{{Maximize}} -
- \overbrace{E_{Norm}(S_j)}^{{Minimize}} )$}
+ \centering{$\textbf{\emph {\textcolor{red}{MaxDist}}} = \max_{j=1,2,\dots ,F}
+ (\overbrace{P_{Norm}(S_j)}^{{\textcolor{blue}{Maximize}}} -
+ \overbrace{E_{Norm}(S_j)}^{{\textcolor{blue}{Minimize}}} )$}
\end{block}
\end{femtoBlock}
\begin{femtoBlock}{}
\begin{itemize}
\small
- \item Our experiments are executed on the simulator SimGrid/SMPI v3.10.\medskip
- \item Our algorithm is applied to NAS parallel benchmarks.\medskip
+ \item The experiments are executed on the simulator SimGrid/SMPI v3.10.\medskip
+ \item The proposed algorithm is applied to the NAS parallel benchmarks.\medskip
\item Each node in the cluster has 18 frequency values from \textbf{2.5$GHz$} to \textbf{800$MHz$}.\medskip
- \item We run the classes A, B and C on 4, 8 or 9 and 16 nodes respectively.\medskip
- \item The dynamic power with the highest frequency is equal to \textbf{20 $W$} and the power static is equal to \textbf{4 $W$}.
+ \item The proposed algorithm was evaluated over the A, B, C classes of the benchmarks using 4, 8 or 9 and 16 nodes respectively. \medskip
+ \item $P_d=20W$, $P_s=4W$.
\end{itemize}
\end{femtoBlock}
\end{frame}
\begin{frame}{Contribution}
-\subsection{\small {3.2 Energy optimization of heterogeneous platform}}
+\section{\small {Energy optimization of heterogeneous platform}}
\begin{center}
-\bf \textcolor{black}{Second contribution} \\
-\vspace{1cm}
-\bf \Large \textcolor{blue}{Energy optimization of Heterogeneous platform}
+
+
+\bf \Large \textcolor{blue}{Energy optimization of a parallel application with iterations running over Heterogeneous platform}
\end{center}
\end{frame}
\begin{frame}{Objectives}
\begin{femtoBlock}{} \vspace{-12 mm}
\begin{itemize} \small
- \item Evaluating the \textcolor{blue}{new energy and performance models} of message passing applications with iterations running
+ \item Proposing \textcolor{blue}{new energy and performance models} for message passing applications with iterations running
over a heterogeneous platform (cluster and Grid). \medskip
- \item Study the effect of the scaling factor $S$ on both \textcolor{blue}{energy consumption and the performance} of
+ \item Studying the effect of the scaling factor $S$ on both the \textcolor{blue}{energy consumption and the performance} of
message passing iterative applications. \medskip
- \item Computing the vector of scaling factors ($S_1, S_2, ..., S_n$) producing \textcolor{blue} {optimal trade-off} between
- energy consumption and performance.
+ \item Computing the vector of scaling factors ($S_1, S_2, ..., S_n$) producing \textcolor{blue} {the optimal trade-off} between
+ the energy consumption and the performance.
\end{itemize}
\vspace{-10 mm}
%% SLIDE 25 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{The energy consumption model}
- -The overall energy consumption of a message passing synchronous distributed application executed over a
- heterogeneous platform is computed as follows:
+ The overall energy consumption of a message passing synchronous application executed over
+ a heterogeneous platform can be computed as follows:
\begin{multline}
\label{eq:energy}
\textcolor{red}{E} = \textcolor{blue}{\sum_{i=1}^{N} {(S_i^{-2} \cdot Pd_i \cdot Tcp_i)}} + {} \\
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 27 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The trade-off between energy and performance}
- \vspace{-7 mm}
- \begin{figure}
- \centering{ \includegraphics[width=.4\textwidth]{c2/heter}}
- \end{figure}
- \vspace{-7 mm}
- \textcolor{red}{\underline{Step1}}: computing the normalized energy \textcolor{blue}{$E_{norm} = \frac{E_{reduced}}
- {E_{Max}}$}. \\
- \textcolor{red}{\underline{Step2}}: computing the normalized performance \textcolor{blue}{$P_{norm} = \frac{T_{Max}}{T_{new}}$}.
+%\begin{frame}{The trade-off between energy and performance}
+ % \vspace{-7 mm}
+ %\begin{figure}
+ % \centering{ \includegraphics[width=.4\textwidth]{c2/heter}}
+ % \end{figure}
+ % \vspace{-7 mm}
+ % \textcolor{red}{\underline{Step1}}: computing the normalized energy \textcolor{blue}%{$E_{norm} = \frac{E_{reduced}}
+ %{E_{Max}}$}. \\
+ % \textcolor{red}{\underline{Step2}}: computing the normalized performance \textcolor{blue}{$P_{norm} = \frac{T_{Max}}{T_{new}}$}.
- \begin{block}{\small The tradeoff model}
- \begin{equation}
- \label{eq:max}
- \textcolor{red}{MaxDist} =
- \mathop {\max_{i=1,\dots F}}_{j=1,\dots,N}
- (\overbrace{P_{norm}(S_{ij})}^{\text{\textcolor{blue}{Maximize}}} -
- \overbrace{E_{norm}(S_{ij})}^{\text{\textcolor{blue}{Minimize}}} )
- \end{equation}
- \end{block}
-\end{frame}
+ % \begin{block}{\small The tradeoff model}
+ % \begin{equation}
+ % \label{eq:max}
+ % \textcolor{red}{MaxDist} =
+ % \mathop {\max_{i=1,\dots F}}_{j=1,\dots,N}
+ % (\overbrace{P_{norm}(S_{ij})}^{\text{\textcolor{blue}{Maximize}}} -
+ % \overbrace{E_{norm}(S_{ij})}^{\text{\textcolor{blue}{Minimize}}} )
+ %\end{equation}
+ % \end{block}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 30 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Experiments over heterogeneous cluster }
+\begin{frame}{Experiments over a heterogeneous cluster }
\begin{itemize}
\small
\item The experiments executed on the simulator SimGrid/SMPI v3.10.\medskip
\item The scaling algorithm was applied to the NAS parallel benchmarks class C.\medskip
\item Four types of processors with different computing powers were used.\medskip
\item We ran the benchmarks on different number of nodes ranging from 4 to 144 nodes.\medskip
- \item The total power consumption of the chosen CPUs is composed of $80\%$ for dynamic power and $20\%$ for static power.
+ \item The total power consumption of the chosen CPUs assumed to be composed of $80\%$ for the dynamic power and $20\%$ for the static power.
\medskip
\end{itemize}
\centering
\includegraphics[width=0.8\textwidth]{c2/energy_saving.pdf}
- \textcolor{blue}{On average, it saves the energy consumption by \textcolor{red}{29\%}
- of NAS benchmarks class C executed over 8 nodes}
+ \textcolor{blue}{On average, it reduces the energy consumption by \textcolor{red}{29\%}
+ for the class C of the NAS benchmarks executed over 8 nodes}
\end{figure}
\end{frame}
\includegraphics[width=.8\textwidth]{c2/perf_degra.pdf}
- \textcolor{blue}{On average, it degrades the performance by \textcolor{red}{3.8\%}
+ \textcolor{blue}{On average, it degrades by \textcolor{red}{3.8\%} the performance
of NAS benchmarks class C executed over 8 nodes}
\end{figure}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 33 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The results of the three powers scenarios}
+\begin{frame}{The results of the three power scenarios}
\vspace{-5 mm}
\begin{figure}[!t]
\centering
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 34 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The comparing our method}
- The proposed method (MaxDist) was compared to the EDP algorithm that minimizes the \textcolor{blue}{
- $\mathit{energy}\times \mathit{delay}$} value.
+\begin{frame}{Comparing the objective function to EDP}
+
+ EDP is the products between the energy consumption and the delay.
\vspace{-5 mm}
\begin{figure}[!t]
\centering
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 35 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Energy optimization of grid platform}
- \begin{figure}[!t]
- \centering
- \includegraphics[width=.6\textwidth]{c2/grid5000.pdf}
+%\begin{frame}{Energy optimization of grid platform}
+ % \begin{figure}[!t]
+ % \centering
+ % \includegraphics[width=.6\textwidth]{c2/grid5000.pdf}
- \small 10 sites distributed over France and Luxembourg
- \end{figure}
-\end{frame}
+ % \small 10 sites distributed over France and Luxembourg
+ %\end{figure}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 36 %%
%%%%%%%%%%%%%%%%%%%%
- \begin{frame}{Performance, Energy and trade-off models} \small
- \begin{block}{\small The performance model of grid}
- \begin{equation}
- \label{eq:perf}
- \Tnew = \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M_i}({\TcpOld[ij]} \cdot S_{ij})
- +\mathop{\min_{j=1,\dots,M_h}} (\Tcm[hj])
-\end{equation}
- \end{block}
+\begin{frame}{The grid architecture}
+\begin{center}
+\includegraphics[width=.8\textwidth]{c2/init_freq.pdf}
+\end{center}
+
+ %\begin{frame}{Performance, Energy and trade-off models} \small
+ %\begin{block}{\small The performance model of grid}
+ % \begin{equation}
+ %\label{eq:perf}
+ %\Tnew = \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M_i}({\TcpOld[ij]} \cdot S_{ij})
+ % +\mathop{\min_{j=1,\dots,M_h}} (\Tcm[hj])
+%\end{equation}
+ %\end{block}
- \begin{block}{\small The energy model of grid}\small
- \begin{equation}
- \label{eq:energy}
- E = \sum_{i=1}^{N} \sum_{i=1}^{M_i} {(S_{ij}^{-2} \cdot \Pd[ij] \cdot \Tcp[ij])} +
- \sum_{i=1}^{N} \sum_{j=1}^{M_i} (\Ps[ij] \cdot \Tnew)
-\end{equation}
- \end{block}
-
-\begin{block}{\small The trade-off model of grid}
-\small
- \begin{equation}
- \label{eq:max}
- \MaxDist =
- \mathop{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M_i}}_{k=1,\dots,F_j}
- (\overbrace{\Pnorm(S_{ijk})}^{\text{Maximize}} -
- \overbrace{\Enorm(S_{ijk})}^{\text{Minimize}} )
-\end{equation}
- \end{block}
+ %\begin{block}{\small The energy model of grid}\small
+ % \begin{equation}
+ %\label{eq:energy}
+ %E = \sum_{i=1}^{N} \sum_{i=1}^{M_i} {(S_{ij}^{-2} \cdot \Pd[ij] \cdot \Tcp[ij])} +
+% \sum_{i=1}^{N} \sum_{j=1}^{M_i} (\Ps[ij] \cdot \Tnew)
+%\end{equation}
+ % \end{block}
+
+%\begin{block}{\small The trade-off model of grid}
+%\small
+ %\begin{equation}
+ %\label{eq:max}
+ %\MaxDist =
+ %\mathop{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M_i}}_{k=1,\dots,F_j}
+ % (\overbrace{\Pnorm(S_{ijk})}^{\text{Maximize}} -
+ % \overbrace{\Enorm(S_{ijk})}^{\text{Minimize}} )
+%\end{equation}
+ % \end{block}
+
\end{frame}
\includegraphics[width=.5\textwidth]{c2/grid5000-2.pdf}
\vspace{-3 mm}
- \textcolor{blue}{The experiments executed over one site and two sites scenarios}
+ \textcolor{blue}{Two experiments were conducted: over one site and two sites
+ each one with three clusters }
\vspace{1mm}
\includegraphics[width=.5\textwidth]{c2/power_consumption.pdf}
- \textcolor{blue}{We used Grid'5000 power measurement tools}
+ \textcolor{blue}{Grid'5000 power measurement tools were used}
\end{frame}
\begin{frame}{Experiments over Grid'5000}
\begin{minipage}{0.4\textwidth}
- \textcolor{blue}{Execution the NAS class D on 16 nodes saves the energy by
- \textcolor{red}{30\%}}
+ %\textcolor{blue}{Execution the NAS class D on 16 nodes saves the energy by
+ %\textcolor{red}{30\%}}
+ \textcolor{blue}{The energy saving = \textcolor{red}{30\%}}
\end{minipage}
\begin{minipage}{0.55\textwidth}
\begin{figure}[h!]
\end{minipage}
\begin{minipage}{0.4\textwidth}
- \textcolor{blue}{Execution the NAS class D on 16 nodes degrades the
- performance by \textcolor{red}{3.2\%}}
+ %\textcolor{blue}{Execution the NAS class D on 16 nodes degrades the
+ %performance by \textcolor{red}{3.2\%}}
+ \textcolor{blue}{The performance degradation = \textcolor{red}{3.2\%}}
\end{minipage}
\begin{minipage}{0.55\textwidth}
\begin{figure}[h!]
\includegraphics[width=.48\textwidth]{c2/per_d_mc.eps}
\end{figure}
- \centering \small \textcolor{blue}{Using multi-core per node scenario decreases the computations to communications ratio}.
+ \centering \small \textcolor{blue}{Using multi-cores per node scenario decreases the computations to communications ratio}.
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 40 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Continuation}
-\subsection{\small {3.3 Energy optimization of asynchronous applications}}
+\begin{frame}{Contribution}
+\section{\small {Energy optimization of asynchronous applications}}
\begin{center}
-\bf \textcolor{black}{Third contribution} \\
-\vspace{1cm}
-\bf \Large \textcolor{blue}{Energy optimization of asynchronous applications}
+\bf \Large \textcolor{blue}{Energy optimization of asynchronous message passing iterative applications}
\end{center}
\end{frame}
%% SLIDE 41 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Problem definition}\vspace{0.8 mm}
-\textcolor{blue}{Execution the parallel iterative application with synchronous communications }
+\textcolor{blue}{The execution of a synchronous parallel iterative application over a grid }
\vspace{-8 mm}
\begin{figure}
\animategraphics[autopause,controls,scale=0.25,buttonsize=0.2cm]{10}{syn/a-}{0}{503}
%% SLIDE 42 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Problem definition}\vspace{0.8 mm}
-\textcolor{blue}{Execution the parallel iterative application with synchronous communications }
+\textcolor{blue}{The execution of an asynchronous parallel iterative application over a grid }
\vspace{-8 mm}
\begin{figure}
\animategraphics[autopause,controls,scale=0.25,buttonsize=0.2cm]{10}{asyn/a-}{0}{440}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 44 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The performance models}
+%\begin{frame}{The performance models}
-\begin{block}{\small The performance model of Asynch. Applications}\small
-\begin{equation}
- \label{eq:asyn_time}
- \Tnew = \frac{\sum_{i=1}^{N} \sum_{j=1}^{M_i}({\TcpOld[ij]} \cdot S_{ij})} {N \cdot M_i }
-\end{equation}
-\end{block}
+%\begin{block}{\small The performance model of Asynch. Applications}\small
+%\begin{equation}
+ %\label{eq:asyn_time}
+ %\Tnew = \frac{\sum_{i=1}^{N} \sum_{j=1}^{M_i}({\TcpOld[ij]} \cdot S_{ij})} {N \cdot M_i }
+%\end{equation}
+%\end{block}
-\begin{block}{\small The performance model of Hybrid Applications}\small
-\begin{equation}
- \label{eq:asyn_perf}
- \Tnew = \frac{\sum_{i=1}^{N} (\max_{j=1,\dots, M_i} ({\TcpOld[ij]} \cdot S_{ij}) +
- \min_{j=1,\dots,M_i} ({\Ltcm[ij]}))}{N}
-\end{equation}
-\end{block}
+%\begin{block}{\small The performance model of Hybrid Applications}\small
+%\begin{equation}
+ %\label{eq:asyn_perf}
+ %\Tnew = \frac{\sum_{i=1}^{N} (\max_{j=1,\dots, M_i} ({\TcpOld[ij]} \cdot S_{ij}) +
+ %\min_{j=1,\dots,M_i} ({\Ltcm[ij]}))}{N}
+%\end{equation}
+%\end{block}
-\end{frame}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 45 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The energy consumption models}
+%\begin{frame}{The energy consumption models}
-\begin{block}{\small The energy model of Asynch. Applications}\small
-\begin{equation}
- \label{eq:asyn_energy1}
- E = \sum_{i=1}^{N} \sum_{j=1}^{M_i} {(S_{ij}^{-2} \cdot \Tcp[ij] \cdot (\Pd[ij]+\Ps[ij]) )}
-\end{equation}
-\end{block}
+%\begin{block}{\small The energy model of Asynch. Applications}\small
+%\begin{equation}
+ %\label{eq:asyn_energy1}
+% E = \sum_{i=1}^{N} \sum_{j=1}^{M_i} {(S_{ij}^{-2} \cdot \Tcp[ij] \cdot (\Pd[ij]+\Ps[ij]) )}
+%\end{equation}
+%\end{block}
-\begin{block}{\small The energy model of Hybrid Applications}\small
-\begin{multline}
- \label{eq:asyn_energy}
- E = \sum_{i=1}^{N} \sum_{j=1}^{M_i} {(S_{ij}^{-2} \cdot \Pd[ij] \cdot \Tcp[ij])} + \sum_{i=1}^{N} \sum_{j=1}^{M_i} (\Ps[ij] \cdot \\
- ( \mathop{\max_{j=1,\dots,M_i}} ({\Tcp[ij]} \cdot S_{ij}) + \mathop{\min_{j=1,\dots,M_i}} ({\Ltcm[ij]})))
-\end{multline}
-\end{block}
+%\begin{block}{\small The energy model of Hybrid Applications}\small
+%\begin{multline}
+ %\label{eq:asyn_energy}
+ %E = \sum_{i=1}^{N} \sum_{j=1}^{M_i} {(S_{ij}^{-2} \cdot \Pd[ij] \cdot \Tcp[ij])} + \sum_{i=1}^{N} \sum_{j=1}^{M_i} (\Ps[ij] \cdot \\
+% ( \mathop{\max_{j=1,\dots,M_i}} ({\Tcp[ij]} \cdot S_{ij}) + \mathop{\min_{j=1,\dots,M_i}} ({\Ltcm[ij]})))
+%\end{multline}
+%\end{block}
+%\end{frame}
+
+
+
+%%%%%%%%%%%%%%%%%%%%
+%% SLIDE 44 %%
+%%%%%%%%%%%%%%%%%%%%
+\begin{frame}{The performance and the energy models }
+
+\centering
+\includegraphics[width=0.9\textwidth]{syn-vs-asyn.pdf}
\end{frame}
+
+
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 46 %%
%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 47 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{The experimental results}
+\begin{frame}{The experiments}
\vspace{-5 mm}
\begin{figure}[!t]
- \centering
+ \begin{itemize}
+ \small
+ \item The architecture of the grid:
+ \end{itemize}
\includegraphics[width=0.5\textwidth]{c3/hybrid-model.pdf}
\end{figure}
\begin{itemize}
\small
- \item Execution the iterative multi-splitting method over simulated Grid.
- \item Execution the iterative multi-splitting method over Grid'5000 test-bed.
+ \item Applying the proposed algorithm to the asynchronous iterative message passing multi-splitting method.
+ \item Evaluating the application over the simulator and Grid'5000.
\end{itemize}
\end{frame}
%% SLIDE 48 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{The simulation results}
-\centering \small \textcolor{blue}{The best scenario in term of energy and performance is the Async. MS with Sync. DVFS}
+\centering \small \textcolor{blue}{The best scenario in terms of energy and performance is the Async. MS with Sync. DVFS}
\centering
- \includegraphics[scale=0.46]{c3/energy_saving.eps}
+ \includegraphics[scale=0.42]{c3/energy_saving.eps}
\centering The average of energy saving = \textcolor{red}{22\%}
\end{frame}
\begin{frame}{The simulation results}
\centering
- \includegraphics[scale=0.46]{c3/perf_degra.eps}
+ \includegraphics[scale=0.42]{c3/perf_degra.eps}
- \centering The average of speed-up = \textcolor{red}{5.72\%}
+ \centering The average speed-up = \textcolor{red}{5.72\%}
\end{frame}
\end{figure}
\vspace{-5 mm}
\centering
- The energy saving = \textcolor{red}{26.93\%}, speeds up = \textcolor{red}{21.48\%}
+The energy saving = \textcolor{red}{26.93\%}, the average speed-up = \textcolor{red}{21.48\%}
\end{frame}
%% SLIDE 52 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Conclusions}
-\section{Conclusions}
+\section{Conclusions and Perspectives}
\begin{itemize}
-\small \barrow We have proposed \textcolor{blue}{a new energy consumption and performance} models for
- synchronous and asynchronous parallel applications with iterations.
-
+\small \barrow Three \textcolor{blue}{ new energy consumption and performance} models were proposed for synchronous and asynchronous parallel applications with iterations running over
+\textcolor{blue}{homogeneous and heterogeneous clusters and grids}.
-\small \barrow The parallel applications with iterations were executed over different parallel architectures such as: \textcolor{blue}{homogeneous cluster, heterogeneous cluster and
-grid}.
-\small \barrow We have proposed \textcolor{blue}{new objective function} to optimize both the energy consumption and the performance.
+
+\small \barrow \textcolor{blue}{A new objective function} was proposed to optimize both the energy consumption and the performance.
\small \barrow \textcolor{blue}{New online frequency selecting algorithms} for clusters and grids were developed.
\small \barrow The proposed algorithms were applied to the \textcolor{blue}{NAS parallel benchmarks} and \textcolor{blue}{the
Multi-splitting} method.
-\small \barrow The proposed algorithms were evaluated over the \textcolor{blue}{SimGrid simulator and over Grid'5000 testbed}.
+\small \barrow The proposed algorithms were evaluated over the \textcolor{blue}{SimGrid simulator} and over \textcolor{blue}{Grid'5000 testbed}.
-\small \barrow All the proposed methods were compared with either \textcolor{blue}{Rauber and Rünger method} or \textcolor{blue}{EDP objective function}.
+\small \barrow All the proposed methods were compared to either \textcolor{blue}{Rauber and Rünger's method} or \textcolor{blue}{the EDP objective function}.
\end{itemize}
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%% SLIDE 53 %%
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-\begin{frame}{Publication}
+\begin{frame}{Publications}
\begin{block}{\small Journal Articles }\scriptsize
\begin{enumerate}[$\lbrack$1$\rbrack$]
%% SLIDE 54 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Perspectives}
-\section{Perspectives}
\begin{itemize}
-\small \barrow We will adapt the proposed algorithms to take into consideration the
+\small \barrow The proposed algorithms should take into consideration the
\textcolor{blue}{variability between some iterations}.
\small \barrow The proposed algorithms should be applied to \textcolor{blue}{other message passing methods with iterations} in order to see how they adapt to the characteristics of these methods.
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Fin} \vspace{-10 mm}
- \centering \Large \textcolor{blue}{Thanks for Your Listening}
+ \centering \Large \textcolor{blue}{Thank you for your listening}
\vspace{2cm}
\centering \textcolor{blue}{ {\Large Questions?}}
