X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e4b14590c5b948dac64c900d8d83e990a3f79122..c535d2d4b5927973fabfb0ea4d99de5df953ed20:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 640f3ae..2c7217b 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -135,35 +135,7 @@ iterative  asynchronous algorithms  to solve  a given  problem on  a large-scale
 simulated environment challenges to  find optimal configurations giving the best
 results with a lowest residual error and in the best of execution time.
 
-<<<<<<< HEAD
-To our knowledge, there is no existing work on the large-scale simulation of a
-real AIAC application. The aim of this paper is twofold. First we give a first
-approach of the simulation of AIAC algorithms using a simulation tool (i.e. the
-SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of
-asynchronous mode algorithms by comparing their performance with the synchronous
-mode. More precisely, we had implemented a program for solving large
-linear system of equations by numerical method GMRES (Generalized
-Minimal Residual) \cite{ref1}. We show, that with minor modifications of the
-initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a
-real AIAC application on different computing architectures. The simulated
-results we obtained are in line with real results exposed in ??\AG[]{ref?}.
-SimGrid had allowed us to launch the application from a modest computing
-infrastructure by simulating different distributed architectures composed by
-clusters nodes interconnected by variable speed networks. In the simulated environment, after setting appropriate 
-network and cluster parameters like the network bandwidth, latency or the processors power, 
-the experimental results have demonstrated a asynchronous execution time saving up to \np[\%]{40} in
-compared to the synchronous mode.
-\AG{Il faudrait revoir la phrase prÃ©cÃ©dente (couper en deux?).  LÃ , on peut
-  avoir l'impression que le gain de \np[\%]{40} est entre une exÃ©cution rÃ©elle
-  et une exÃ©cution simulÃ©e!}
-\CER{La phrase a Ã©tÃ© modifiÃ©e}
-
-This article is structured as follows: after this introduction, the next  section will give a brief description of
-iterative asynchronous model.  Then, the simulation framework SimGrid is presented with the settings to create various
-distributed architectures. The algorithm of  the multisplitting method based on GMRES \LZK{??? GMRES n'utilise pas la mÃ©thode de multisplitting! Sinon ne doit on pas expliquer le choix d'une mÃ©thode de multisplitting?} \CER{La phrase a Ã©tÃ© corrigÃ©e} written with MPI primitives and
-its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results
-carried out will be presented before some concluding remarks and future works.
-=======
+
 To our knowledge,  there is no existing work on the  large-scale simulation of a
 real  AIAC application.   {\bf  The contribution  of  the present  paper can  be
   summarised  in two  main  points}.  First  we  give a  first  approach of  the
@@ -196,7 +168,7 @@ based  on GMRES to  solve each  block obtained  of the  splitting. This  code is
 written with MPI  primitives and its adaptation to  SimGrid with SMPI (Simulated
 MPI) is  detailed in the next  section. At last, the  simulation results carried
 out will be presented before some concluding remarks and future works.
->>>>>>> 6785b9ef58de0db67c33ca901c7813f3dfdc76e0
+
  
 \section{Motivations and scientific context}
 
@@ -476,13 +448,13 @@ and with the addition of the primitive MPI\_Test was needed to avoid a memory fa
 \CER{On voulait en fait montrer la simplicitÃ© de l'adaptation de l'algo a SimGrid. Les problÃ¨mes rencontrÃ©s dÃ©crits dans ce paragraphe concerne surtout le mode async}\LZK{OK. J'aurais prÃ©fÃ©rÃ© avoir un peu plus de dÃ©tails sur l'adaptation de la version async} 
 \CER{Le problÃ¨me majeur sur l'adaptation MPI vers SMPI pour la partie asynchrone de l'algorithme a Ã©tÃ© le plantage en SMPI de Waitall aprÃ¨s un Isend et Irecv. J'avais proposÃ© un workaround en utilisant un MPI\_wait sÃ©parÃ© pour chaque Ã©change a la place d'un waitall unique pour TOUTES les Ã©changes, une instruction qui semble bien fonctionner en MPI. Ce workaround aussi fonctionne bien. Mais aprÃ¨s, tu as modifiÃ© le programme avec l'ajout d'un MPI\_Test, au niveau de la routine de dÃ©tection de la convergence et du coup, l'Ã©change global avec waitall a aussi fonctionnÃ©.}
 Note here that the use of SMPI functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation.
-As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
-global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 
-shared memory used by threads simulating each computing unit in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had
-also to be reviewed.
+As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, the scope of all declared 
+global variables have been moved to local to subroutine. Indeed, global variables generate side effects arising from the concurrent access of 
+shared memory used by threads simulating each computing unit in the SimGrid architecture. 
+%Second, the alignment of certain types of variables such as ``long int'' had also to be reviewed.
 \AG{Ã propos de ces problÃ¨mes d'alignement, en dire plus si Ã§a a un intÃ©rÃªt, ou l'enlever.}
-\CER{Ce problÃ¨me fait partie des modifications que j'ai dÃ» faire dans l'adaptation du programme MPI vers SMPI. IL dÃ©coule de la diffÃ©rence de la taille des mots en mÃ©moire : en 32 bits, pour les variables declarees en long int, on garde dans les instructions de sortie (printf, sprintf, ...) le format \%lu sinon en 64 bits, on le substitue par \%llu.} 
- Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
+\CER{Ce problÃ¨me fait partie des modifications que j'ai dÃ» faire dans l'adaptation du programme MPI vers SMPI. IL dÃ©coule de la diffÃ©rence de la taille des mots en mÃ©moire : en 32 bits, pour les variables declarees en long int, on garde dans les instructions de sortie (printf, sprintf, ...) le format \%lu sinon en 64 bits, on le substitue par \%llu. La phrase a Ã©tÃ© enlevÃ©.} 
+Second, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 
 environment. We have successfully executed the code in synchronous mode using parallel GMRES algorithm compared with our multisplitting algorithm in asynchronous mode after few modifications. 
 
@@ -665,9 +637,9 @@ Note that the program was run with the following parameters:
 
 	- Processor unit power : 1.5 GFlops;
 
-	- Intracluster network : bandwidth = 1,25 Gbits/s and latency = 5E-05 ms;
+	- Intracluster network : bandwidth = 1,25 Gbits/s and latency = \np{E-5} ms;
 
-	- Intercluster network : bandwidth = 5 Mbits/s and latency = 5E-03 ms;
+	- Intercluster network : bandwidth = 5 Mbits/s and latency = 5.\np{E-3} ms;
 \end{itemize}
 
 
