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[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 6e262b194a8a4ff0f5a98518892b14e5c041fff7..9962b2520c1685670573b4c95dc01761af6c82ad 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -79,7 +79,7 @@ network parameters is not easy  because with supercomputers such parameters are
 fixed. So one  solution consists in using simulations first  in order to analyze
 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
 fixed. So one  solution consists in using simulations first  in order to analyze
 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
+of asynchronous  iterative algorithms. For that,  we compare the  behavior of a

 synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
 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
 synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
 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


@@ -232,13 +232,13 @@ asynchronous iterative algorithms comes from the fact it is necessary to run the
 with real data. In fact, from an execution to another the order of messages will
 change and the  number of iterations to reach the  convergence will also change.
 According  to all  the parameters  of the  platform (number  of nodes,  power of
 with real data. In fact, from an execution to another the order of messages will
 change and the  number of iterations to reach the  convergence will also change.
 According  to all  the parameters  of the  platform (number  of nodes,  power of

-nodes,  inter  and  intra clusrters  bandwith  and  latency, etc.) and  of  the
+nodes,  inter  and  intra clusters  bandwidth  and  latency, etc.) and  of  the

 algorithm  (number   of  splittings  with  the   multisplitting  algorithm),  the
 multisplitting code  will obtain the solution  more or less  quickly. Of course,
 the GMRES method also depends of the same parameters. As it is difficult to have
 access to  many clusters,  grids or supercomputers  with many  different network
 parameters,  it  is  interesting  to  be  able  to  simulate  the  behaviors  of
 algorithm  (number   of  splittings  with  the   multisplitting  algorithm),  the
 multisplitting code  will obtain the solution  more or less  quickly. Of course,
 the GMRES method also depends of the same parameters. As it is difficult to have
 access to  many clusters,  grids or supercomputers  with many  different network
 parameters,  it  is  interesting  to  be  able  to  simulate  the  behaviors  of

-asynchronous iterative algoritms before being able to runs real experiments.
+asynchronous iterative algorithms before being able to run real experiments.

 
 
 
 
 
 


@@ -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}
 


@@ -632,7 +645,9 @@ Note that the program was run with the following parameters:
 
 \begin{itemize}
 \item HOSTFILE: Text file containing the list of the processors units name. Here 100 hosts;
 
 \begin{itemize}
 \item HOSTFILE: Text file containing the list of the processors units name. Here 100 hosts;

-\item PLATFORM: XML file description of the platform architecture whith the following characteristics: %two clusters (cluster1 and cluster2) with the following characteristics :
+\item PLATFORM: XML file description of the platform architecture with the
+  following characteristics:
+  % two clusters (cluster1 and cluster2) with the following characteristics:

   \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};


@@ -745,6 +760,6 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
 % LocalWords:  Ouest Vieille Talence cedex scalability experimentations HPC MPI
 % LocalWords:  Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
 % LocalWords:  Fortran GFlops priori Mbit de du fcomte multisplitting scalable
 % LocalWords:  Ouest Vieille Talence cedex scalability experimentations HPC MPI
 % LocalWords:  Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
 % LocalWords:  Fortran GFlops priori Mbit de du fcomte multisplitting scalable

-% LocalWords:  SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib
+% LocalWords:  SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib Gbit

 % LocalWords:  intra durations nonsingular Waitall discretization discretized
 % LocalWords:  intra durations nonsingular Waitall discretization discretized

-% LocalWords:  InnerSolver Isend Irecv
+% LocalWords:  InnerSolver Isend Irecv parallelization