corrections

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index a0748686ed142a659afe89e68f5046f23c97b45a..e73d2ccc01ef901aa81ce40d84695f952a1d5c20 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -82,8 +82,8 @@ what parameters  could influence or not  the behaviors of an  algorithm. In this
 paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
 of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
 synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
 paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
 of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
 synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with

-simulations  in  which we  choose  some parameters.   Both  codes  are real  MPI
-codes. Simulations allow us to see when the multisplitting algorithm can be more
+simulations  which let us easily choose  some parameters.   Both  codes  are real  MPI
+codes and simulations allow us to see when the asynchronous multisplitting algorithm can be more

 efficient than the GMRES one to solve a 3D Poisson problem.
 
 
 efficient than the GMRES one to solve a 3D Poisson problem.
 
 


@@ -103,7 +103,7 @@ suggests, these algorithms solve a given problem by successive iterations ($X_{n
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
 
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
 

-Parallelization of such algorithms generally involve the division of the problem
+Parallelization of such algorithms generally involves the division of the problem

 into  several  \emph{blocks}  that  will  be  solved  in  parallel  on  multiple
 processing units. The latter will communicate each intermediate results before a
 new  iteration starts  and until  the  approximate solution  is reached.   These
 into  several  \emph{blocks}  that  will  be  solved  in  parallel  on  multiple
 processing units. The latter will communicate each intermediate results before a
 new  iteration starts  and until  the  approximate solution  is reached.   These


@@ -384,7 +384,7 @@ exchanged by message passing using MPI non-blocking communication routines.
 \begin{figure}[!t]
 \centering
   \includegraphics[width=60mm,keepaspectratio]{clustering}
 \begin{figure}[!t]
 \centering
   \includegraphics[width=60mm,keepaspectratio]{clustering}

-\caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.}
+\caption{Example of three distant clusters of processors.}

 \label{fig:4.1}
 \end{figure}
 
 \label{fig:4.1}
 \end{figure}
 


@@ -518,7 +518,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     & 5         & 5         & 5         & 5         & 5         \\
     \hline
     latency (ms)
     & 5         & 5         & 5         & 5         & 5         \\
     \hline
     latency (ms)

-    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      \\
+    & 20      &  20      & 20      & 20      & 20      \\

     \hline
     power (GFlops)
     & 1         & 1         & 1         & 1.5       & 1.5       \\
     \hline
     power (GFlops)
     & 1         & 1         & 1         & 1.5       & 1.5       \\


@@ -543,7 +543,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
     latency (ms)
     & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
     latency (ms)

-    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\ %      & 0.03      & 0.01 \\
+    & 20      & 20      & 20      & 20      & 20 \\ %      & 0.03      & 0.01 \\

     \hline
     Power (GFlops)
     & 1.5       & 1.5       & 1.5       & 1.5       & 1.5 \\ %      & 1         & 1.5 \\
     \hline
     Power (GFlops)
     & 1.5       & 1.5       & 1.5       & 1.5       & 1.5 \\ %      & 1         & 1.5 \\


@@ -561,13 +561,15 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
   \end{mytable}
 \end{table}
   
   \end{mytable}
 \end{table}
   

+\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la table?}
+

 %Then we have changed the network configuration using three clusters containing
 %respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the
 %clusters. In the same way as above, a judicious choice of key parameters has
 %permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the
 %relative gains greater than 1 with a matrix size from 62 to 100 elements.
 
 %Then we have changed the network configuration using three clusters containing
 %respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the
 %clusters. In the same way as above, a judicious choice of key parameters has
 %permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the
 %relative gains greater than 1 with a matrix size from 62 to 100 elements.
 

-\CER{En accord avec RC, on a pour le moment enlevé les tableaux 2 et 3 sachant que les résultats obtenus sont limites. De même, on a enlevé aussi les deux dernières colonnes du tableau I en attendant une meilleure performance et une meilleure precision}
+%\CER{En accord avec RC, on a pour le moment enlevé les tableaux 2 et 3 sachant que les résultats obtenus sont limites. De même, on a enlevé aussi les deux dernières colonnes du tableau I en attendant une meilleure performance et une meilleure precision}

 %\begin{table}[!t]
 %  \centering
 %  \caption{3 clusters, each with 33 nodes}
 %\begin{table}[!t]
 %  \centering
 %  \caption{3 clusters, each with 33 nodes}


@@ -634,8 +636,8 @@ Note that the program was run with the following parameters:
   \begin{itemize}
   \item 2 clusters of 50 hosts each;
   \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5};
   \begin{itemize}
   \item 2 clusters of 50 hosts each;
   \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5};

-  \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{0.05};
-  \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[$\mu$s]{20};
+  \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{50};
+  \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[ms]{20};

   \end{itemize}
 \end{itemize}
 
   \end{itemize}
 \end{itemize}
 


@@ -692,30 +694,28 @@ elements.
 \section{Conclusion}
 The simulation of the execution of parallel asynchronous iterative algorithms on large scale  clusters has been presented. 
 In this work, we show that SIMGRID is an efficient simulation tool that allows us to 
 \section{Conclusion}
 The simulation of the execution of parallel asynchronous iterative algorithms on large scale  clusters has been presented. 
 In this work, we show that SIMGRID is an efficient simulation tool that allows us to 

-reach the following three objectives: 
+reach the following two objectives: 

 
 \begin{enumerate}
 
 \begin{enumerate}

-\item To have a flexible configurable execution platform resolving the 
-hard exercise to access to very limited but so solicited physical 
-resources;
-\item to ensure the algorithm convergence with a reasonable time and
-iteration number ;
-\item and finally and more importantly, to find the correct combination 
-of the cluster and network specifications permitting to save time in 
-executing the algorithm in asynchronous mode.
+\item  To have  a flexible  configurable execution  platform that  allows  us to
+  simulate algorithms for  which execution of all parts of
+  the  code is  necessary. Using  simulations before  real executions  is  a nice
+  solution to detect potential scalability problems.
+
+\item To test the combination of the cluster and network specifications permitting to execute an asynchronous algorithm faster than a synchronous one.

 \end{enumerate}
 \end{enumerate}

-Our results have shown that in certain conditions, asynchronous mode is 
-speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
+Our results have shown that with two distant clusters, the asynchronous multisplitting is faster to \np[\%]{40} compared to the synchronous GMRES method

 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 
 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 

- Several studies have already addressed the performance execution time of 
+Several studies have already addressed the performance execution time of 

 this class of algorithm. The work presented in this paper has 
 demonstrated an original solution to optimize the use of a simulation 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 
 this class of algorithm. The work presented in this paper has 
 demonstrated an original solution to optimize the use of a simulation 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 

-\LZK{Perspectives???}
+In future works, we plan to extend our experimentations to larger scale platforms by increasing the number of computing cores and the number of clusters. 
+We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to experimentally validate our study.

 
 \section*{Acknowledgment}
 
 
 \section*{Acknowledgment}