From: Arnaud Giersch <arnaud.giersch@univ-fcomte.fr>
Date: Wed, 7 May 2014 16:08:16 +0000 (+0200)
Subject: Spelling.
X-Git-Tag: hpcc2014_submission~3
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/commitdiff_plain/a6eb13d9e879fac3b6ad0e97391bfea86b6c51a8

Spelling.
---

diff --git a/hpcc.tex b/hpcc.tex
index 83753da..9962b25 100644
--- 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
-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
@@ -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
-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
-asynchronous iterative algoritms before being able to runs real experiments.
+asynchronous iterative algorithms before being able to run real experiments.
 
 
 
@@ -645,7 +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;
-\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};
@@ -758,6 +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:  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:  InnerSolver Isend Irecv
+% LocalWords:  InnerSolver Isend Irecv parallelization