Fix numbers and units again. Use LaTeX itemize environment.

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 2c7217b48d16c2936087fa8391e04fb55cdbc135..ae3a2290086e9cd5f9193ce27ca87e69571101b8 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -451,9 +451,6 @@ Note here that the use of SMPI functions optimizer for memory footprint and CPU
 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. 
 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{À propos de ces problèmes d'alignement, en dire plus si ça a un intérêt, ou l'enlever.}
-\CER{Ce problème fait partie des modifications que j'ai dû faire dans l'adaptation du programme MPI vers SMPI. IL découle de la différence de la taille des mots en mémoire : 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 été enlevé.} 

 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. 
 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. 


@@ -476,10 +473,6 @@ 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.
   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 ça dure plus
-    longtemps (que ça aille moins vite) en asynchrone qu'en synchrone ?
-    Ce n'est pas plutôt 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
 \end{itemize}
 
 A priori, obtaining a relative gain greater than 1 would be difficult in a local


@@ -512,51 +505,51 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
   \caption{2 clusters, each with 50 nodes}
   \label{tab.cluster.2x50}
 
   \caption{2 clusters, each with 50 nodes}
   \label{tab.cluster.2x50}
 

-  \begin{mytable}{6}
+  \begin{mytable}{5}

     \hline
     \hline

-    bandwidth (Mbits/s)
-    & 5         & 5         & 5         & 5         & 5         & 50 \\
+    bandwidth (Mbit/s)
+    & 5         & 5         & 5         & 5         & 5         \\

     \hline
     latency (ms)
     \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)
     \hline
     power (GFlops)

-    & 1         & 1         & 1         & 1.5       & 1.5       & 1.5 \\
+    & 1         & 1         & 1         & 1.5       & 1.5       \\

     \hline
     size
     \hline
     size

-    & 62        & 62        & 62        & 100       & 100       & 110 \\
+    & 62        & 62        & 62        & 100       & 100       \\

     \hline
     Precision
     \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
     \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
 
     \hline
   \end{mytable}
 
   \bigskip
 

-  \begin{mytable}{6}
+  \begin{mytable}{5}

     \hline
     \hline

-    bandwidth (Mbits/s)
-    & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
+    bandwidth (Mbit/s)
+    & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\

     \hline
     latency (ms)
     \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)
     \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
     \hline
     size

-    & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\
+    & 110       & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\

     \hline
     Precision
     \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
     \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}
     \hline
   \end{mytable}
 \end{table}


@@ -628,18 +621,16 @@ Note that the program was run with the following parameters:
 
 \paragraph*{SMPI parameters}
 
 
 \paragraph*{SMPI parameters}
 

-~\\{}\AG{Donner un peu plus de précisions (plateforme en particulier).}
-\CER {Précisions ajoutées}
-

 \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 :
 \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 = \np{E-5} ms;
-
-       - Intercluster network : bandwidth = 5 Mbits/s and latency = 5.\np{E-3} 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}
 
 
 \end{itemize}
 
 


@@ -668,7 +659,7 @@ 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
 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.
 
 high external precision of \np{E-11} for a matrix size from 110 to 150 side
 elements.