X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAhmed.git/blobdiff_plain/3dfa5bd4d94afee02f58fc41b9e95b4f2fc78133..c0e283963e036526dd575326d4f257b270ac03e9:/thesis-presentation/AhmedSlides.tex?ds=sidebyside

diff --git a/thesis-presentation/AhmedSlides.tex b/thesis-presentation/AhmedSlides.tex
index 2a57d28..6dbbd81 100644
--- a/thesis-presentation/AhmedSlides.tex
+++ b/thesis-presentation/AhmedSlides.tex
@@ -112,18 +112,13 @@
 %%%%%%%%%%%%%%%%%%%%
 %%    SLIDE 03    %%
 %%%%%%%%%%%%%%%%%%%% 
-\begin{frame}{Introduction and problem definition}
+\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}
@@ -175,7 +170,8 @@
     \end{minipage}%
     \vspace{0.2cm}
     \begin{minipage}{0.5\textwidth} 
-     \textcolor{blue}{2)} \small \bf \textcolor{black}{Increase the number of 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.}  
           
@@ -289,10 +285,10 @@ for a warehouse-sized computer.
         
                 \begin{itemize}   \small \justifying
                  
-                   \item   Studying the effect of the scaling factor on the \textbf{energy consumption and performance } of parallel  applications with iterations. \medskip
+                   \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   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.
                    
                    
@@ -307,6 +303,38 @@ for a warehouse-sized computer.
 
 
 
+%%%%%%%%%%%%%%%%%%%%
+%%    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}
+
+
+
+
+
+
+
+  
+
+
  
  
  
@@ -316,32 +344,34 @@ for a warehouse-sized computer.
 %%%%%%%%%%%%%%%%%%%% 
 \begin{frame}{Energy model for a homogeneous platform}    
       The power consumed by a processor is divided into two power metrics: the dynamic (\textcolor{red}{$P_d$}) and  the static   
-       (\textcolor{red}{$P_s$}) power. 
+       (\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].} 
+   	     
    	     
-   	     The frequency scaling factor is the ratio between the maximum and the new frequency, \textcolor{blue}{$S = \frac{F_{max}}{F_{new}}$}.
 \end{frame}
 
+
+  
 %%%%%%%%%%%%%%%%%%%%
 %%    SLIDE 12   %%
 %%%%%%%%%%%%%%%%%%%% 
 
 \begin{frame}{Energy model for a homogeneous platform}
-       \vspace{-0.77cm}
+       \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}
@@ -362,26 +392,6 @@ for a warehouse-sized computer.
           
        
 \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}
 
 
 
@@ -443,7 +453,7 @@ for a warehouse-sized computer.
 %%%%%%%%%%%%%%%%%%%%
 %%    SLIDE 17   %%
 %%%%%%%%%%%%%%%%%%%% 
-\begin{frame}{Experimental results }
+\begin{frame}{Experiment over SimGrid }
       \begin{femtoBlock}{}      
         \begin{itemize}
          \small
@@ -478,20 +488,21 @@ 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}}}
          
-            \includegraphics[width=.55\textwidth]{c1/compare-c.pdf}}
+         \vspace{2 mm}
+         
+           $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}
 
 
@@ -567,7 +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}
                  
@@ -889,7 +900,7 @@ for a warehouse-sized computer.
 %%%%%%%%%%%%%%%%%%%%
 \begin{frame}{Comparing the objective function to EDP}
      
-     EDP is the products between the energy consumption and the delay.
+     EDP is the product between the energy consumption and the delay \tiny\textsuperscript{3}.
     \vspace{-5 mm}
     \begin{figure}[!t]
     \centering
@@ -897,6 +908,8 @@ for a warehouse-sized computer.
     
   
     \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}
@@ -1039,11 +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}
 
 
 
@@ -1169,7 +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}
@@ -1212,6 +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}
