X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/7314cfe257c8b75f34a34995a4a2075edc1d3888..8795c25e6f799826141cea21050391987f86f3ae:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index 865fb94..b33be48 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -445,16 +445,14 @@ We did not encounter major blocking problems when adapting the multisplitting al
 debugging. Indeed, apart from the review of the program sequence for asynchronous exchanges between processors within a cluster or between clusters, the algorithm was executed successfully with SMPI and provided identical outputs as those obtained with direct execution under MPI. In synchronous 
 mode, the execution of the program raised no particular issue but in asynchronous mode, the review of the sequence of MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions
 and with the addition of the primitive MPI\_Test was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm.
-\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©.}
+%\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.
-\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.
+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, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
+\AG{compilation or run-time error?}
 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. 
 
@@ -476,10 +474,6 @@ study that the results depend on the following parameters:
   compared to the asynchronous mode ($t_\text{sync} / t_\text{async}$) is defined as the \emph{relative gain}. So,
   our objective running the algorithm in SimGrid is to obtain a relative gain
   greater than 1.
-  \AG{$t_\text{async} / t_\text{sync} > 1$, l'objectif est donc que Ã§a dure plus
-    longtemps (que Ã§a aille moins vite) en asynchrone qu'en synchrone ?
-    Ce n'est pas plutÃ´t l'inverse ?}
-  \CER{J'ai modifie la phrase.}
 \end{itemize}
 
 A priori, obtaining a relative gain greater than 1 would be difficult in a local
@@ -496,7 +490,7 @@ The algorithm was run on a two clusters based network with 50 hosts each, totali
 factors have provided the results shown in Table~\ref{tab.cluster.2x50}. The algorithm convergence with a 3D
 matrix size ranging from $N_x = N_y = N_z = \text{62}$ to 150 elements (that is from
 $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
-\text{\np{3375000}}$ entries), is obtained in asynchronous in average 2.5 times speeder than the synchronous mode. 
+\text{\np{3375000}}$ entries), is obtained in asynchronous in average 2.5 times faster than in the synchronous mode.
 \AG{Expliquer comment lire les tableaux.}
 \CER{J'ai reformulÃ© la phrase par la lecture du tableau. Plus de dÃ©tails seront lus dans la partie InterprÃ©tations et commentaires}
 % use the same column width for the following three tables
@@ -512,51 +506,51 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
   \caption{2 clusters, each with 50 nodes}
   \label{tab.cluster.2x50}
 
-  \begin{mytable}{6}
+  \begin{mytable}{5}
     \hline
-    bandwidth (Mbits/s)
-    & 5         & 5         & 5         & 5         & 5         & 50 \\
+    bandwidth (Mbit/s)
+    & 5         & 5         & 5         & 5         & 5         \\
     \hline
     latency (ms)
-    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      \\
     \hline
     power (GFlops)
-    & 1         & 1         & 1         & 1.5       & 1.5       & 1.5 \\
+    & 1         & 1         & 1         & 1.5       & 1.5       \\
     \hline
-    size
-    & 62        & 62        & 62        & 100       & 100       & 110 \\
+    size $(n^3)$
+    & 62        & 62        & 62        & 100       & 100       \\
     \hline
     Precision
-    & \np{E-5}   & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} \\
     \hline
     \hline
     Relative gain
-    & 2.52     & 2.55     & 2.52     & 2.57     & 2.54     & 2.53 \\
+    & 2.52      & 2.55      & 2.52      & 2.57      & 2.54      \\
     \hline
   \end{mytable}
 
   \bigskip
 
-  \begin{mytable}{6}
+  \begin{mytable}{5}
     \hline
-    bandwidth (Mbits/s)
-    & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
+    bandwidth (Mbit/s)
+    & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
     latency (ms)
-    & 0.02      & 0.02      & 0.02      & 0.02 \\ %      & 0.03      & 0.01 \\
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\ %      & 0.03      & 0.01 \\
     \hline
     Power (GFlops)
-    & 1.5       & 1.5       & 1.5       & 1.5 \\ %      & 1         & 1.5 \\
+    & 1.5       & 1.5       & 1.5       & 1.5       & 1.5 \\ %      & 1         & 1.5 \\
     \hline
-    size
-    & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\
+    size $(n^3)$
+    & 110       & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\
     \hline
     Precision
-    & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5}  & \np{E-5} \\
+    & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5}  & \np{E-5} \\
     \hline
     \hline
     Relative gain
-    & 2.51     & 2.58     & 2.55     & 2.54   \\ %  & 1.59      & 1.29 \\
+    & 2.53      & 2.51     & 2.58     & 2.55     & 2.54   \\ %  & 1.59      & 1.29 \\
     \hline
   \end{mytable}
 \end{table}
@@ -628,31 +622,30 @@ Note that the program was run with the following parameters:
 
 \paragraph*{SMPI parameters}
 
-~\\{}\AG{Donner un peu plus de prÃ©cisions (plateforme en particulier).}
-\CER {PrÃ©cisions ajoutÃ©es}
-
 \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 : two clusters (cluster1 and cluster2) with the following characteristics :
-
-	- Processor unit power : 1.5 GFlops;
-
-	- Intracluster network : bandwidth = 1,25 Gbits/s and latency = 5E-05 ms;
-
-	- Intercluster network : bandwidth = 5 Mbits/s and latency = 5E-03 ms;
+  \begin{itemize}
+  \item Processor unit power: \np[GFlops]{1.5};
+  \item Intracluster network bandwidth: \np[Gbit/s]{1.25} and latency:
+    \np[$\mu$s]{0.05};
+  \item Intercluster network bandwidth: \np[Mbit/s]{5} and latency:
+    \np[$\mu$s]{5};
+  \end{itemize}
 \end{itemize}
 
 
 \paragraph*{Arguments of the program}
 
 \begin{itemize}
-	\item Description of the cluster architecture matching the format <Number of cluster> <Number of hosts in cluster\_1> <Number of hosts in cluster\_2>;
-	\item Maximum number of iterations;
-	\item Precisions on the residual error;
-	\item Matrix size $N_x$, $N_y$ and $N_z$;
-	\item Matrix diagonal value: \np{1.0} 	(See (3));
-	\item Matrix off-diagonal value: $-\frac{1}{6}$ 	(See(3));
-	\item Communication mode: Asynchronous.
+\item Description of the cluster architecture matching the format <Number of
+  cluster> <Number of hosts in cluster1> <Number of hosts in cluster2>;
+\item Maximum number of iterations;
+\item Precisions on the residual error;
+\item Matrix size $N_x$, $N_y$ and $N_z$;
+\item Matrix diagonal value: \np{1.0} (See~(\ref{eq:03}));
+\item Matrix off-diagonal value: \np{-1}/\np{6} (See~(\ref{eq:03}));
+\item Communication mode: asynchronous.
 \end{itemize}
 
 \paragraph*{Interpretations and comments}
@@ -668,7 +661,7 @@ obtained in asynchronous mode for a matrix size of 62 elements. It is noticed th
 stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
 increasing the matrix size up to 100 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\AG[]{2.5 ?} is obtained with
+\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
 elements.
 
@@ -687,10 +680,10 @@ elements.
 %obtained with a bandwidth of \np[Mbit/s]{1} as shown in
 %Table~\ref{tab.cluster.3x67}.
 
-\RC{Est ce qu'on sait expliquer pourquoi il y a une telle diffÃ©rence entre les rÃ©sultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas trÃ¨s bons... Je me demande s'il ne faut pas les enlever...}
-\RC{En fait je pense avoir la rÃ©ponse Ã  ma remarque... On voit avec les 2 clusters que le gain est d'autant plus grand qu'on choisit une bonne prÃ©cision. Donc, plusieurs solutions, lancer rapidement un long test pour confirmer ca, ou enlever des tests... ou on ne change rien :-)}
-\LZK{Ma question est: le bandwidth et latency sont ceux inter-clusters ou pour les deux inter et intra cluster??}
-\CER{DÃ©finitivement, les paramÃ¨tres rÃ©seaux variables ici se rapportent au rÃ©seau INTER cluster.}
+%\RC{Est ce qu'on sait expliquer pourquoi il y a une telle diffÃ©rence entre les rÃ©sultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas trÃ¨s bons... Je me demande s'il ne faut pas les enlever...}
+%\RC{En fait je pense avoir la rÃ©ponse Ã  ma remarque... On voit avec les 2 clusters que le gain est d'autant plus grand qu'on choisit une bonne prÃ©cision. Donc, plusieurs solutions, lancer rapidement un long test pour confirmer ca, ou enlever des tests... ou on ne change rien :-)}
+%\LZK{Ma question est: le bandwidth et latency sont ceux inter-clusters ou pour les deux inter et intra cluster??}
+%\CER{DÃ©finitivement, les paramÃ¨tres rÃ©seaux variables ici se rapportent au rÃ©seau INTER cluster.}
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
 asynchronous mode on an environment simulating a large scale of virtual 
