Concluding sentence.

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index d85a9179d909e0aad0a45012490a7853da1291f3..83753da163938e5424590613cb5f4189d1c6d3aa 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -1,4 +1,3 @@
-

 \documentclass[conference]{IEEEtran}
 
 \usepackage[T1]{fontenc}
 \documentclass[conference]{IEEEtran}
 
 \usepackage[T1]{fontenc}


@@ -163,7 +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
 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

 
 
 
 
 
 


@@ -261,24 +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
 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
 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
 
 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
 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


@@ -286,6 +288,17 @@ 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.
 
 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}.  All these works conclude that
+SimGrid is able to simulate pretty accurately the real behavior of the
+applications.
+
+

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 


@@ -509,7 +522,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
 \begin{table}[!t]
   \centering
   \caption{Relative gain  of the multisplitting algorithm compared  to GMRES for
 \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}
   \label{tab.cluster.2x50}
 
   \begin{mytable}{5}


@@ -517,14 +530,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 5         & 5         & 5         & 5         & 5         \\
     \hline
     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 $(N)$
     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} \\
     \hline
     Precision
     & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} \\


@@ -542,14 +555,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
     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 $(N)$
     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} \\
     \hline
     Precision
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5}  & \np{E-5} \\


@@ -561,7 +574,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ 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?}
+%\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
 
 %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


@@ -662,14 +675,14 @@ 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
 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
 CPU power of \np[\%]{50} to \np[GFlops]{1.5} to get the algorithm convergence and the same order of asynchronous mode efficiency.  Maintaining such processor power but increasing network throughput inter cluster up to
 \np[Mbit/s]{50}, the result of efficiency with a relative gain of 2.5 is obtained with
 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
 CPU power of \np[\%]{50} to \np[GFlops]{1.5} to get the algorithm convergence and the same order of asynchronous mode efficiency.  Maintaining such processor power but increasing network throughput inter cluster up to
 \np[Mbit/s]{50}, the result of efficiency with a relative gain of 2.5 is obtained with

-high external precision of \np{E-11} for a matrix size from 110 to 150 side
+high external precision of \np{E-11} for a matrix size from $110^3$ to $150^3$ side

 elements.
 
 %For the 3 clusters architecture including a total of 100 hosts,
 elements.
 
 %For the 3 clusters architecture including a total of 100 hosts,


@@ -679,8 +692,8 @@ 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}.
 %(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
 
 %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


@@ -693,7 +706,7 @@ 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. 
 %\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}
 reach the following two objectives: 
 
 \begin{enumerate}


@@ -704,7 +717,7 @@ reach the following two objectives:
 
 \item To test the combination of the cluster and network specifications permitting to execute an asynchronous algorithm faster than a synchronous one.
 \end{enumerate}
 
 \item To test the combination of the cluster and network specifications permitting to execute an asynchronous algorithm faster than a synchronous one.
 \end{enumerate}

-Our results have shown that with two distant clusters, the asynchronous multisplitting is faster to \np[\%]{40} compared to the synchronous GMRES method
+Our results have shown that with two distant clusters, the asynchronous multisplitting method 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.
 


@@ -715,7 +728,7 @@ 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. 
 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.
+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}
 
 
 \section*{Acknowledgment}