X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/757a3d8174e2736033052c4acc64de277cd66e11..0d6c7fbfb35dbfc041886ff260a29ce8da9017d0:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index da2ec91..b9e6bf3 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -162,7 +162,8 @@ network  platforms  are   the  bandwidth  and  the  latency   of  inter  cluster
 network. Parameters on the cluster's architecture are the number of machines and
 the  computation power  of a  machine.  Simulations show  that the  asynchronous
 multisplitting algorithm  can solve the  3D Poisson problem  approximately twice
-faster than GMRES with two distant clusters.
+faster than GMRES with two distant clusters. In this way, we present an original solution to optimize the use of a simulation 
+tool to run efficiently an  asynchronous iterative parallel algorithm in a grid architecture
 
 
 
@@ -260,24 +261,26 @@ run real applications written in MPI~\cite{MPI}.  Apart from the native C
 interface, SimGrid provides bindings for the C++, Java, Lua and Ruby programming
 languages.  SMPI is the interface that has been used for the work exposed in
 this paper.  The SMPI interface implements about \np[\%]{80} of the MPI 2.0
-standard~\cite{bedaride:hal-00919507}, and supports applications written in C or
-Fortran, with little or no modifications.
+standard~\cite{bedaride+degomme+genaud+al.2013.toward}, and supports
+applications written in C or Fortran, with little or no modifications.
 
-Within SimGrid, the execution of a distributed application is simulated on a
-single machine.  The application code is really executed, but some operations
+Within SimGrid, the execution of a distributed application is simulated by a
+single process.  The application code is really executed, but some operations
 like the communications are intercepted, and their running time is computed
 according to the characteristics of the simulated execution platform.  The
 description of this target platform is given as an input for the execution, by
 the mean of an XML file.  It describes the properties of the platform, such as
 the computing nodes with their computing power, the interconnection links with
-their bandwidth and latency, and the routing strategy.  The simulated running
-time of the application is computed according to these properties.
+their bandwidth and latency, and the routing strategy.  The scheduling of the
+simulated processes, as well as the simulated running time of the application is
+computed according to these properties.
 
 To compute the durations of the operations in the simulated world, and to take
 into account resource sharing (e.g. bandwidth sharing between competing
 communications), SimGrid uses a fluid model.  This allows to run relatively fast
 simulations, while still keeping accurate
-results~\cite{bedaride:hal-00919507,tomacs13}.  Moreover, depending on the
+results~\cite{bedaride+degomme+genaud+al.2013.toward,
+  velho+schnorr+casanova+al.2013.validity}.  Moreover, depending on the
 simulated application, SimGrid/SMPI allows to skip long lasting computations and
 to only take their duration into account.  When the real computations cannot be
 skipped, but the results have no importance for the simulation results, there is
@@ -285,6 +288,14 @@ also the possibility to share dynamically allocated data structures between
 several simulated processes, and thus to reduce the whole memory consumption.
 These two techniques can help to run simulations at a very large scale.
 
+The validity of simulations with SimGrid has been asserted by several studies.
+See, for example, \cite{velho+schnorr+casanova+al.2013.validity} and articles
+referenced therein for the validity of the network models.  Comparisons between
+real execution of MPI applications on the one hand, and their simulation with
+SMPI on the other hand, are presented in~\cite{guermouche+renard.2010.first,
+  clauss+stillwell+genaud+al.2011.single,
+  bedaride+degomme+genaud+al.2013.toward}.
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 
@@ -508,7 +519,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
 \begin{table}[!t]
   \centering
   \caption{Relative gain  of the multisplitting algorithm compared  to GMRES for
-    different configurations with 2 clusters, each one composed of 50 nodes.}
+    different configurations with 2 clusters, each one composed of 50 nodes. Latency = $20$ms}
   \label{tab.cluster.2x50}
 
   \begin{mytable}{5}
@@ -516,14 +527,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 5         & 5         & 5         & 5         & 5         \\
     \hline
-    latency (ms)
-    & 20      &  20      & 20      & 20      & 20      \\
-    \hline
+  %  latency (ms)
+   % & 20      &  20      & 20      & 20      & 20      \\
+    %\hline
     power (GFlops)
     & 1         & 1         & 1         & 1.5       & 1.5       \\
     \hline
     size $(N)$
-    & 62        & 62        & 62        & 100       & 100       \\
+    & $62^3$        & $62^3$        & $62^3$        & $100^3$       & $100^3$       \\
     \hline
     Precision
     & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} \\
@@ -541,14 +552,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
-    latency (ms)
-    & 20      & 20      & 20      & 20      & 20 \\ %      & 0.03      & 0.01 \\
-    \hline
+    %latency (ms)
+    %& 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
     size $(N)$
-    & 110       & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\
+    & $110^3$       & $120^3$       & $130^3$       & $140^3$       & $150^3$  \\ %     & 171       & 171 \\
     \hline
     Precision
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5}  & \np{E-5} \\
@@ -560,7 +571,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
   \end{mytable}
 \end{table}
   
-\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la 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
@@ -661,8 +672,8 @@ the results have given a relative gain more than 2.5, showing the effectiveness
 asynchronous multisplitting  compared to GMRES with two distant clusters.
 
 With these settings, Table~\ref{tab.cluster.2x50} shows
-that after setting the bandwidth of the  inter cluster network to  \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
-of one GFlops, an efficiency of about \np[\%]{40} is
+that after setting the bandwidth of the  inter cluster network to  \np[Mbit/s]{5}, the latency to $20$ millisecond and the processor power
+to one GFlops, an efficiency of about \np[\%]{40} is
 obtained in asynchronous mode for a matrix size of $62^3$ elements. It is noticed that the result remains
 stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
 increasing the matrix size up to $100^3$ elements, it was necessary to increase the
@@ -678,8 +689,8 @@ elements.
 %(synchronous and asynchronous) is achieved with an inter cluster of
 %\np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency greater than 1.2 with a matrix %size of 100 points, it was necessary to degrade the
 %inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.
-\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ???
-  Quelle est la perte de perfs en faisant ça ?}
+%\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ???
+  %Quelle est la perte de perfs en faisant ça ?}
 
 %A last attempt was made for a configuration of three clusters but more powerful
 %with 200 nodes in total. The convergence with a relative gain around 1.1 was
@@ -692,7 +703,7 @@ elements.
 %\CER{Définitivement, les paramètres réseaux variables ici se rapportent au réseau INTER cluster.}
 \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 
+In this work, we show that SimGrid is an efficient simulation tool that allows us to 
 reach the following two objectives: 
 
 \begin{enumerate}
@@ -714,7 +725,7 @@ tool to run efficiently an iterative parallel algorithm in asynchronous
 mode in a grid architecture. 
 
 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. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods.
+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 better experimentally validate our study. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods.
 
 \section*{Acknowledgment}