X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAhmed.git/blobdiff_plain/1e2dba6a4221941261eb9fc136576db92c7b1e56..5db01fd07f0d6d36e46b6adb74d4d49f636c9669:/thesis-presentation/AhmedSlides.tex?ds=inline diff --git a/thesis-presentation/AhmedSlides.tex b/thesis-presentation/AhmedSlides.tex index 9bb4b6f..6dbbd81 100644 --- a/thesis-presentation/AhmedSlides.tex +++ b/thesis-presentation/AhmedSlides.tex @@ -22,7 +22,7 @@ \usepackage{fixltx2e} %% used to put some subscripts lower, and make them more legible \newcommand{\fxheight}[1]{\ifx#1\relax\relax\else\rule{0pt}{1.52ex}#1\fi} - +\usepackage{ragged2e} \newcommand{\CL}{\Xsub{C}{L}} \newcommand{\Dist}{\mathit{Dist}} \newcommand{\EdNew}{\Xsub{E}{dNew}} @@ -109,13 +109,55 @@ \tableofcontents \end{frame} +%%%%%%%%%%%%%%%%%%%% +%% SLIDE 03 %% +%%%%%%%%%%%%%%%%%%%% +\begin{frame}{Definition of parallel computing} +\section{\small {Introduction and Problem definition}} + \centering + \includegraphics[width=0.99\textwidth]{para.pdf} +\end{frame} + + + +\begin{frame}{Execution of synchronous parallel tasks} +\vspace{-0.5 cm} +\begin{figure} + \centering + \subfloat[Synchronous imbalanced communications]{% + \includegraphics[scale=0.49]{c1/commtasks}\label{fig:h1}} + \subfloat[Synchronous imbalanced computations]{% + \includegraphics[scale=0.49]{c1/compt}\label{fig:h2}} + % \caption{Parallel tasks on homogeneous platform} + \label{fig:homo} +\end{figure} + \end{frame} + + %%%%%%%%%%%%%%%%%%%% +%% SLIDE 07 %% +%%%%%%%%%%%%%%%%%%%% + + +\begin{frame}{\large Synchronous and asynchronous iterative methods } +\vspace{-0.5 cm} +\begin{figure} + +\includegraphics[scale=0.42]{syn_tasks.pdf} +\vspace{0.6 cm} +\includegraphics[scale=0.42]{Asyn_tasks.pdf} +\end{figure} + + + \end{frame} + + %%%%%%%%%%%%%%%%%%%% %% SLIDE 03 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{Introduction and problem definition} - \section{\small {Introduction and Problem definition}} - \bf \textcolor{blue}{To get more computing power:} +\begin{frame}{Approaches to get more computing power} + + %\bf \textcolor{blue}{} \begin{minipage}{0.5\textwidth} \textcolor{blue}{1)} \small \bf \textcolor{black}{Increase the frequency of a processor.\\ (limited due to overheating)} \end{minipage}% @@ -128,7 +170,8 @@ \end{minipage}% \vspace{0.2cm} \begin{minipage}{0.5\textwidth} - \textcolor{blue}{2)} \small \bf \textcolor{black}{Use more nodes.} + \textcolor{blue}{2)} \small \bf \textcolor{black}{Increase the number of computing + units.} \textcolor{black}{The supercomputer Tianhe-2 has more than 3 million cores and consumes around 17.8 megawatts.} @@ -142,7 +185,6 @@ - %%%%%%%%%%%%%%%%%%% %% SLIDE 04 %% %%%%%%%%%%%%%%%%%%%% @@ -151,26 +193,27 @@ \textcolor{blue}{1)} \bf \textcolor{black}{Switch-off idle nodes method} \vspace{-0.9cm} \begin{figure} - \animategraphics[autopause,loop,controls,scale=0.25,buttonsize=0.2cm]{200}{on-off/a-}{0}{69} + \animategraphics[autopause,controls,scale=0.26,buttonsize=0.2cm]{200}{on-off/a-}{0}{111} %\includegraphics[width=0.6\textwidth]{on-off/a-69} \end{figure} \end{frame} %%%%%%%%%%%%%%%%%%%% -%% SLIDE 06 %% +%% SLIDE 05 %% %%%%%%%%%%%%%%%%%%%% \begin{frame}{Techniques for energy consumption reduction} - \textcolor{blue}{2)} \bf \textcolor{black}{Dynamic voltage and frequency Scaling (DVFS)} + \textcolor{blue}{2)} \bf \textcolor{black}{Dynamic Voltage and Frequency Scaling (DVFS)} \vspace{-0.9cm} \begin{figure} - \animategraphics[autopause,controls,scale=0.25,buttonsize=0.2cm]{10}{DVFS-meq/a-}{0}{109} + \animategraphics[autopause,controls,scale=0.26,buttonsize=0.2cm]{10}{DVFS-meq/a-}{0}{175} %\includegraphics[width=0.6\textwidth]{DVFS-meq/a-109} \end{figure} \end{frame} - - +%%%%%%%%%%%%%%%%%%%% +%% SLIDE 06 %% +%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%% %% SLIDE 07 %% %%%%%%%%%%%%%%%%%%%% @@ -203,7 +246,7 @@ \begin{block}{\textcolor{white}{Challenge and Objective}} - \small \textcolor{blue}{Challenge:} \textcolor{black}{DVFS is used to reduce the energy consumption, \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 also degrades the performance of the CPU.} \vspace{0.1cm} \small \textcolor{blue}{Objective:} \textcolor{black}{Applying the DVFS to minimize the energy consumption while maintaining the performance of the parallel application.} @@ -239,43 +282,59 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% \begin{frame}{Objectives} - \begin{femtoBlock}{} \vspace{-12 mm} - \begin{itemize} \small - \item Study the effect of the scaling factor on the \textbf{energy consumption and performance } of parallel applications with iterations. \medskip + + \begin{itemize} \small \justifying + + \item Studying the effect of the frequency scaling on the \textbf{energy consumption and performance } of parallel applications with iterations. \medskip - \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 that produces \textbf {the optimal trade-off} between the energy consumption and the performance. \medskip - \item Comparing the proposed algorithm to existing methods. + \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 that produces \textbf {the good 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{} - \vspace{-10 mm} - \end{femtoBlock} + + \end{frame} + %%%%%%%%%%%%%%%%%%%% -%% SLIDE 10 %% +%% SLIDE 13 %% %%%%%%%%%%%%%%%%%%%% +\begin{frame}{Performance evaluation of MPI programs} + +\small The frequency scaling factor is the ratio between the maximum and the new frequency, \textcolor{blue}{$S = \frac{F_{max}}{F_{new}}$}. + \vspace{5 mm} + + \begin{femtoBlock}{} + \vspace{-5 mm} + \begin{block}{\small Execution time prediction model} + \centering{ $ \textcolor{red}{T_{new}} = \textcolor{blue}{T_{Max Comp Old} \cdot S + T_{{Min Comm Old}}}$} + \end{block} + \vspace{5 mm} + \centering{\includegraphics[width=.4\textwidth]{c1/cg_per} + \quad% + \includegraphics[width=.4\textwidth]{c1/lu_pre}} + \vspace{1 mm} + + \small The maximum normalized error for CG=0.0073 \textbf{(the smallest)} and LU=0.031 \textbf{(the worst)}. + \end{femtoBlock} +\end{frame} -\begin{frame}{Execution of synchronous parallel tasks} -\vspace{-0.5 cm} -\begin{figure} - \centering - \subfloat[Synchronous imbalanced communications]{% - \includegraphics[scale=0.49]{c1/commtasks}\label{fig:h1}} - \subfloat[Synchronous imbalanced computations]{% - \includegraphics[scale=0.49]{c1/compt}\label{fig:h2}} - % \caption{Parallel tasks on homogeneous platform} - \label{fig:homo} -\end{figure} - \end{frame} + + + + + + + @@ -284,68 +343,55 @@ for a warehouse-sized computer. %% SLIDE 11 %% %%%%%%%%%%%%%%%%%%%% \begin{frame}{Energy model for a homogeneous platform} - The power consumed by a processor divided into two power metrics: the dynamic (\textcolor{red}{$P_d$}) and static - (\textcolor{red}{$P_s$}) power. + The power consumed by a processor is divided into two power metrics: the dynamic (\textcolor{red}{$P_d$}) and the static + (\textcolor{red}{$P_s$}) powers. \begin{equation} \label{eq:pd} \textcolor{red}{ P_d} = \textcolor{blue}{\alpha \cdot CL \cdot V^2 \cdot F} \end{equation} \scriptsize \underline{Where}: \\ - \scriptsize {\textcolor{blue}{$\alpha$}: switching activity \hspace{15 mm} \textcolor{blue}{$CL$}: load capacitance\\ - \textcolor{blue}{$V$} the supply voltage \hspace{14 mm} \textcolor{blue}{$F$}: operational frequency} + \scriptsize {\textcolor{blue}{$\alpha$}: switching activity. \hspace{15 mm} \textcolor{blue}{$CL$}: load capacitance [F].\\ + \textcolor{blue}{$V$}: the supply voltage [V]. \hspace{8 mm} \textcolor{blue}{$F$}: operational frequency [Hz].} \begin{equation} \label{eq:ps} \small \textcolor{red}{P_s} = \textcolor{blue}{V \cdot N_{trans} \cdot K_{design} \cdot I_{Leak}} \end{equation} \underline{Where}:\\ - \scriptsize{ \textcolor{blue}{$V$}: the supply voltage. \hspace{28 mm} \textcolor{blue}{$N_{trans}$}: number of transistors. \\ - \textcolor{blue}{$K_{design}$}: design dependent parameter. \hspace{8 mm} \textcolor{blue}{$I_{leak}$}: technology dependent - parameter.} + \scriptsize{ \textcolor{blue}{$V$}: the supply voltage [V]. \hspace{19 mm} \textcolor{blue}{$N_{trans}$}: number of transistors. \\ + \textcolor{blue}{$K_{design}$}: design dependent parameter. \hspace{3 mm} \textcolor{blue}{$I_{leak}$}: technology dependent + parameter [A].} + + \end{frame} + + %%%%%%%%%%%%%%%%%%%% %% SLIDE 12 %% %%%%%%%%%%%%%%%%%%%% \begin{frame}{Energy model for a homogeneous platform} - - The frequency scaling factor is the ratio between the maximum and the new frequency, \textcolor{blue}{$S = \frac{F_{max}}{F_{new}}$}. \medskip - - + \vspace{-0.77cm} + \begin{figure} + \animategraphics[autopause,controls,scale=0.3,buttonsize=0.2cm]{10}{homo-model/a-}{0}{441} + %\includegraphics[width=0.6\textwidth]{homo-model/a-356} + \end{figure} - \begin{block}{\small Rauber and Rünger's energy model} - $ E = P_{d} \cdot S_1^{-2} \cdot - \left( T_1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^2} \right) + - P_{s} \cdot S_1 \cdot T_1 \cdot N$ - \end{block} - \textcolor{blue}{$S_1$}: the maximum scaling factor.\\ - \textcolor{blue}{$P_{d}$}: the dynamic power.\\ - \textcolor{blue}{$P_{s}$}: the static power.\\ - \textcolor{blue}{$T_I$}: the execution time of the slower task.\\ - \textcolor{blue}{$T_i$}: the execution time of task i.\\ - \textcolor{blue}{$N$}: the number of nodes. + % \begin{block}{\small Rauber and Rünger's energy model} + %$ E = P_{d} \cdot S_1^{-2} \cdot + %\left( T_1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^2} \right) + + % P_{s} \cdot S_1 \cdot T_1 \cdot N$ + %\end{block} + % \textcolor{blue}{$S_1$}: the maximum scaling factor.\\ + % \textcolor{blue}{$P_{d}$}: the dynamic power.\\ + % \textcolor{blue}{$P_{s}$}: the static power.\\ + % \textcolor{blue}{$T_I$}: the execution time of the slower task.\\ + % \textcolor{blue}{$T_i$}: the execution time of task i.\\ + % \textcolor{blue}{$N$}: the number of nodes. + + \end{frame} - - -%%%%%%%%%%%%%%%%%%%% -%% SLIDE 13 %% -%%%%%%%%%%%%%%%%%%%% -\begin{frame}{Performance evaluation of MPI programs} - \begin{femtoBlock}{} - \vspace{-5 mm} - \begin{block}{\small Execution time prediction model} - \centering{ $ \textcolor{red}{T_{new}} = \textcolor{blue}{T_{Max Comp Old} \cdot S + T_{{Min Comm Old}}}$} - \end{block} - \vspace{10 mm} - \centering{\includegraphics[width=.4\textwidth]{c1/cg_per} - \quad% - \includegraphics[width=.4\textwidth]{c1/lu_pre}} - \vspace{5 mm} - - \small The maximum normalized error for CG=0.0073 \textbf{(the smallest)} and LU=0.031 \textbf{(the worst)}. - \end{femtoBlock} -\end{frame} @@ -383,23 +429,23 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 15 %% %%%%%%%%%%%%%%%%%%%% - \begin{frame}{Scaling factor selection algorithm} -\vspace{-0.75cm} - \begin{center} - \includegraphics[width=.56 \textwidth]{c1/algo-homo} - \end{center} + %\begin{frame}{Scaling factor selection algorithm} +%\vspace{-0.75cm} + % \begin{center} + %\includegraphics[width=.56 \textwidth]{c1/algo-homo} + %\end{center} -\end{frame} +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 16 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{Scaling algorithm example} +\begin{frame}{Scaling factor selection algorithm} \vspace{-0.75cm} \begin{figure} - \animategraphics[autopause,controls,scale=0.28,buttonsize=0.2cm]{10}{dvfs-homo/a-}{0}{159} + \animategraphics[autopause,controls,scale=0.29,buttonsize=0.2cm]{10}{dvfs-homo/a-}{0}{335} %\includegraphics[width=0.6\textwidth]{dvfs-homo/a-159} \end{figure} \end{frame} @@ -407,7 +453,7 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 17 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{Experimental results } +\begin{frame}{Experiment over SimGrid } \begin{femtoBlock}{} \begin{itemize} \small @@ -431,6 +477,8 @@ for a warehouse-sized computer. \includegraphics[width=.35\textwidth]{c1/cg} \includegraphics[width=.35\textwidth]{c1/bt}} +\hspace{0.5cm} + \centering {\includegraphics[width=.55\textwidth]{c1/results.pdf}} \end{femtoBlock} \end{frame} @@ -440,45 +488,46 @@ for a warehouse-sized computer. %% SLIDE 19 %% %%%%%%%%%%%%%%%%%%%% \begin{frame}{Results comparison} - \begin{block}{\small Rauber and Rünger's optimal scaling factor} - $S_{opt} = \sqrt[3]{\frac{2}{N} \cdot \frac{P_{dyn}}{P_{static}} \cdot - \left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3}\right) } $ - \end{block} - - - \centering { - %\includegraphics[width=.33\textwidth]{c1/c1.pdf} - %\qquad - %\includegraphics[width=.33\textwidth]{c1/c2.pdf}} - + \small \textcolor{blue}{Rauber and Rünger's scaling factor \textcolor{black}{ \tiny \textsuperscript{2}}} + + \vspace{2 mm} - \includegraphics[width=.55\textwidth]{c1/compare-c.pdf}} + $S_{opt} = \sqrt[3]{\frac{2}{N} \cdot \frac{P_{dyn}}{P_{static}} \cdot + \left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3}\right) } $ + + \begin{center} + \includegraphics[width=.55\textwidth]{c1/compare-c.pdf} + \end{center} + + +\vspace{-2 mm} + \tiny \textsuperscript{2} Thomas Rauber and Gudula Rünger. Analytical modeling and simulation of the energy consumption of independent tasks. In Proceedings of the Winter Simulation Conference, 2012. \end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 20 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The proposed new energy model} - \vspace{-0.75cm} - \begin{figure} - \animategraphics[autopause,controls,scale=0.28,buttonsize=0.2cm]{10}{homo-model/a-}{0}{356} +%\begin{frame}{The proposed new energy model} + % \vspace{-0.75cm} + %\begin{figure} + % \animategraphics[autopause,controls,scale=0.28,buttonsize=0.2cm]{10}{homo-model/a-}{0}{356} %\includegraphics[width=0.6\textwidth]{homo-model/a-356} - \end{figure} -\end{frame} + % \end{figure} +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 21 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{\large Comparing the new model with Rauber's model } - \vspace{0.1cm} - \centering - \includegraphics[width=.45\textwidth]{c1/energy_con} +%\begin{frame}{\large Comparing the new model with Rauber's model } +% \vspace{0.1cm} +% \centering + %\includegraphics[width=.45\textwidth]{c1/energy_con} - \includegraphics[width=.5\textwidth]{c1/compare-scales} -\end{frame} + %\includegraphics[width=.5\textwidth]{c1/compare-scales} +%\end{frame} @@ -529,7 +578,7 @@ for a warehouse-sized computer. \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} {the optimal trade-off} between + \item Computing the vector of scaling factors ($S_1, S_2, ..., S_n$) producing \textcolor{blue} {the good trade-off} between the energy consumption and the performance. \end{itemize} @@ -564,27 +613,27 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 25 %% %%%%%%%%%%%%%%%%%%%% - \begin{frame}{The energy consumption model} - 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)}} + {} \\ - \textcolor{blue}{\sum_{i=1}^{N} (Ps_i \cdot (\max_{i=1,2,\dots,N} (Tcp_i \cdot S_{i}) + {\min_{i=1,2,\dots,N} (Tcm_i))}} - \hspace{10 mm} - \end{multline} - \underline{where}:\\ - \textcolor{blue}{N} : is the number of nodes. -\end{frame} + %\begin{frame}{The energy consumption model} + % 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)}} + {} \\ + % \textcolor{blue}{\sum_{i=1}^{N} (Ps_i \cdot (\max_{i=1,2,\dots,N} (Tcp_i \cdot S_{i}) + {\min_{i=1,2,\dots,N} (Tcm_i))}} + % \hspace{10 mm} + % \end{multline} + % \underline{where}:\\ + % \textcolor{blue}{N} : is the number of nodes. +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 26 %% %%%%%%%%%%%%%%%%%%%% - \begin{frame}{The energy model example for heter. cluster} - \vspace{-0.5cm} + \begin{frame}{The energy model for heterogeneous cluster} + \vspace{-0.77cm} \begin{figure} - \animategraphics[autopause,controls,scale=0.28,buttonsize=0.2cm]{10}{heter-model/a-}{0}{272} + \animategraphics[autopause,controls,scale=0.3,buttonsize=0.2cm]{10}{heter-model/a-}{0}{350} %\includegraphics[width=0.6\textwidth]{heter-model/a-272} \end{figure} \end{frame} @@ -620,22 +669,22 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 28 %% %%%%%%%%%%%%%%%%%%%% - \begin{frame}{The scaling algorithm for heter. cluster} + %\begin{frame}{The scaling algorithm for heter. cluster} - \centering - \includegraphics[width=.52\textwidth]{algo-heter} - \end{frame} + %\centering + %\includegraphics[width=.52\textwidth]{algo-heter} + %\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 29 %% %%%%%%%%%%%%%%%%%%%% - \begin{frame}{The scaling algorithm example} - \vspace{-0.5cm} + \begin{frame}{The scaling algorithm for heter. cluster} + \vspace{-0.77cm} \centering \begin{figure} - \animategraphics[autopause,controls,scale=0.28,buttonsize=0.2cm]{10}{dvfs-heter/a-}{0}{650} + \animategraphics[autopause,controls,scale=0.3,buttonsize=0.2cm]{10}{dvfs-heter/a-}{0}{836} % \includegraphics[width=0.6\textwidth]{dvfs-heter/a-650} \end{figure} \end{frame} @@ -646,86 +695,58 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 30 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{Experiments over a heterogeneous cluster } - \begin{itemize} - \small - \item The experiments were 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 The benchmarks were executed with different number of nodes ranging from 4 to 144 nodes.\medskip - \item It was assumed that the total power consumption of the CPU consist of 80\% dynamic power and 20\% static power. - \medskip +%\begin{frame}{Experiments over a heterogeneous cluster } + % \begin{itemize} + % \small + % \item The experiments were 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 The benchmarks were executed with different number of nodes ranging from 4 to 144 nodes.\medskip + % \item It was assumed that the total power consumption of the CPU consist of 80\% dynamic power and 20\% static power. + % \medskip - \end{itemize} + %\end{itemize} -\end{frame} +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 31 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The experimental results} - \vspace{-5 mm} - \begin{figure}[!t] - \centering - \includegraphics[width=0.8\textwidth]{c2/energy_saving.pdf} +%\begin{frame}{The simulation results} + % \vspace{-5 mm} + % \begin{figure}[!t] + %\centering + %\includegraphics[width=0.8\textwidth]{c2/energy_saving.pdf} - \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} + % \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} + % \end{figure} +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 32 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The experimental results} - \vspace{-5 mm} - \begin{figure}[!t] - \centering +%\begin{frame}{The simulation results} + % \vspace{-5 mm} + % \begin{figure}[!t] + % \centering - \includegraphics[width=.8\textwidth]{c2/perf_degra.pdf} + % \includegraphics[width=.8\textwidth]{c2/perf_degra.pdf} - \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} + % \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 power scenarios} - \vspace{-5 mm} - \begin{figure}[!t] - \centering - \includegraphics[width=.55\textwidth]{c2/three_power.pdf} - \vspace{10 mm} - \includegraphics[width=.55\textwidth]{c2/three_scenarios.pdf} - \end{figure} -\end{frame} -%%%%%%%%%%%%%%%%%%%% -%% SLIDE 34 %% -%%%%%%%%%%%%%%%%%%%% -\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 - \includegraphics[width=.55\textwidth]{c2/avg_compare.pdf} - - \includegraphics[width=.55\textwidth]{c2/compare_with_EDP.pdf} - \end{figure} -\end{frame} - - %%%%%%%%%%%%%%%%%%%% @@ -744,10 +765,10 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 36 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The grid architecture} -\begin{center} -\includegraphics[width=.8\textwidth]{c2/init_freq.pdf} -\end{center} +%\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} @@ -779,7 +800,7 @@ for a warehouse-sized computer. % \end{block} - \end{frame} + %\end{frame} @@ -837,11 +858,32 @@ for a warehouse-sized computer. +%%%%%%%%%%%%%%%%%%%% +%% SLIDE 33 %% +%%%%%%%%%%%%%%%%%%%% +\begin{frame}{The results of the three power scenarios} + \vspace{-5 mm} + \begin{figure}[!t] + \centering + \includegraphics[width=.45\textwidth]{c2/eng_pow.eps} + \hspace{0.3cm} + \includegraphics[width=.45\textwidth]{c2/per_pow.eps} + \vspace{4 mm} + \includegraphics[width=.7\textwidth]{c2/three_scenarios.pdf} + \end{figure} +\end{frame} + + + + + + + %%%%%%%%%%%%%%%%%%%% %% SLIDE 39 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{Experiments over Grid'5000} - \textcolor{blue}{One core and Multi-cores per node results:} +\begin{frame}{One core and Multi-cores per node results} + %\textcolor{blue}{One core and Multi-cores per node results:} \begin{figure}[h!] \includegraphics[width=.48\textwidth]{c2/eng_s_mc.eps} @@ -853,7 +895,22 @@ for a warehouse-sized computer. \end{frame} - +%%%%%%%%%%%%%%%%%%%% +%% SLIDE 34 %% +%%%%%%%%%%%%%%%%%%%% +\begin{frame}{Comparing the objective function to EDP} + + EDP is the product between the energy consumption and the delay \tiny\textsuperscript{3}. + \vspace{-5 mm} + \begin{figure}[!t] + \centering + \includegraphics[width=.6\textwidth]{c2/edp_dist.eps} + + + \end{figure} + + \tiny \textsuperscript{3} Spiliopoulos et al, Green governors: A framework for continuously adaptive dvfs, in International Green Computing Conference and Workshops (IGCC), 2011. +\end{frame} %\begin{frame}{Summary} %\begin{itemize} % \small @@ -893,7 +950,7 @@ for a warehouse-sized computer. \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} + \animategraphics[autopause,controls,scale=0.26,buttonsize=0.2cm]{10}{syn/a-}{0}{647} %\includegraphics[width=0.6\textwidth]{syn/a-503} \end{figure} \end{frame} @@ -907,7 +964,7 @@ for a warehouse-sized computer. \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} + \animategraphics[autopause,controls,scale=0.26,buttonsize=0.2cm]{10}{asyn/a-}{0}{556} %\includegraphics[width=0.6\textwidth]{asyn/a-440} \end{figure} \end{frame} @@ -921,7 +978,7 @@ for a warehouse-sized computer. \textcolor{blue}{Using asynchronous communications with DVFS } \vspace{-8 mm} \begin{figure} - \animategraphics[autopause,controls,scale=0.25,buttonsize=0.2cm]{10}{asyn+dvfs/a-}{0}{314} + \animategraphics[autopause,controls,scale=0.26,buttonsize=0.2cm]{10}{asyn+dvfs/a-}{0}{344} %\includegraphics[width=0.6\textwidth]{asyn+dvfs/a-314} \end{figure} \end{frame} @@ -995,11 +1052,11 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 46 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The scaling algorithm for Asynch. applications} -\vspace{-0.1 mm} -\centering -\includegraphics[width=0.55\textwidth]{algo-hybrid.pdf} -\end{frame} +%\begin{frame}{The scaling algorithm for Asynch. applications} +%\vspace{-0.1 mm} +%\centering +%\includegraphics[width=0.55\textwidth]{algo-hybrid.pdf} +%\end{frame} @@ -1027,27 +1084,27 @@ for a warehouse-sized computer. %%%%%%%%%%%%%%%%%%%% %% SLIDE 48 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The simulation results} -\centering \small \textcolor{blue}{The best scenario in terms of energy and performance is the Async. MS with Sync. DVFS} +%\begin{frame}{The simulation results} +%\centering \small \textcolor{blue}{The best scenario in terms of energy and performance is %the Async. MS with Sync. DVFS} -\centering - \includegraphics[scale=0.42]{c3/energy_saving.eps} +%\centering + % \includegraphics[scale=0.42]{c3/energy_saving.eps} - \centering The average energy saving = \textcolor{red}{22\%} -\end{frame} + %\centering The average energy saving = \textcolor{red}{22\%} +%\end{frame} %%%%%%%%%%%%%%%%%%%% %% SLIDE 49 %% %%%%%%%%%%%%%%%%%%%% -\begin{frame}{The simulation results} -\centering +%\begin{frame}{The simulation results} +%\centering - \includegraphics[scale=0.42]{c3/perf_degra.eps} + % \includegraphics[scale=0.42]{c3/perf_degra.eps} - \centering The average speed-up = \textcolor{red}{5.72\%} -\end{frame} +%\centering The average speed-up = \textcolor{red}{5.72\%} +%\end{frame} @@ -1055,7 +1112,7 @@ for a warehouse-sized computer. %% SLIDE 50 %% %%%%%%%%%%%%%%%%%%%% \begin{frame}{The Grid'5000 results} - \vspace{-20 mm} + \vspace{-10 mm} \begin{figure}[!t] \centering \hspace{-8 mm} @@ -1064,6 +1121,9 @@ for a warehouse-sized computer. \end{figure} \vspace{-5 mm} \centering \footnotesize + + %\small \textcolor{blue}{The best scenario in terms of energy and performance is the Async. MS with Sync. DVFS} + The average energy saving = \textcolor{red}{26.93\%}, the average speed-up = \textcolor{red}{21.48\%} \end{frame} @@ -1122,7 +1182,7 @@ Multi-splitting} method. Science}, 2016. \item Ahmed Fanfakh, Jean-Claude Charr, Raphaël Couturier, Arnaud Giersch. Energy Consumption Reduction for - Asynchronous Message Passing Applications. \textit{Journal of Supercomputing}, 2016, (Submitted) + Asynchronous Message Passing Applications. \textit{Journal of Supercomputing}, 2016, (Accepted with minor revisions) \end{enumerate} \end{block} @@ -1165,6 +1225,9 @@ Multi-splitting} method. \small \barrow The proposed algorithms for heterogeneous platforms should be applied to heterogeneous platforms composed of \textcolor{blue}{CPUs and GPUs}. \small \barrow Comparing the results returned by the energy models to the values given by \textcolor{blue}{real instruments that measure the energy consumptions} of CPUs during the execution time. +\small \barrow Considering the power consumed by the other devices in the node such as +\textcolor{blue}{the memory and the hard drive} in the energy consumption model. + \end{itemize} \end{frame} @@ -1174,7 +1237,7 @@ Multi-splitting} method. %%%%%%%%%%%%%%%%%%%% \begin{frame}{Fin} \vspace{-10 mm} - \centering \Large \textcolor{blue}{Thank you for your listening} + \centering \Large \textcolor{blue}{Thank you for your attention} \vspace{2cm} \centering \textcolor{blue}{ {\Large Questions?}}