X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/28e01316fd4048f6765d2771d36cd4565cba5421..0ed25093da14942800a6808b24fe51623cb882d3:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 04c5403..6e262b1 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -1,4 +1,3 @@
-
 \documentclass[conference]{IEEEtran}
 
 \usepackage[T1]{fontenc}
@@ -163,7 +162,8 @@ network  platforms  are   the  bandwidth  and  the  latency   of  inter  cluster
 network. Parameters on the cluster's architecture are the number of machines and
 the  computation power  of a  machine.  Simulations show  that the  asynchronous
 multisplitting algorithm  can solve the  3D Poisson problem  approximately twice
-faster than GMRES with two distant clusters.
+faster than GMRES with two distant clusters. In this way, we present an original solution to optimize the use of a simulation 
+tool to run efficiently an  asynchronous iterative parallel algorithm in a grid architecture
 
 
 
@@ -509,7 +509,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
 \begin{table}[!t]
   \centering
   \caption{Relative gain  of the multisplitting algorithm compared  to GMRES for
-    different configurations with 2 clusters, each one composed of 50 nodes.}
+    different configurations with 2 clusters, each one composed of 50 nodes. Latency = $20$ms}
   \label{tab.cluster.2x50}
 
   \begin{mytable}{5}
@@ -517,14 +517,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 5         & 5         & 5         & 5         & 5         \\
     \hline
-    latency (ms)
-    & 20      &  20      & 20      & 20      & 20      \\
-    \hline
+  %  latency (ms)
+   % & 20      &  20      & 20      & 20      & 20      \\
+    %\hline
     power (GFlops)
     & 1         & 1         & 1         & 1.5       & 1.5       \\
     \hline
     size $(N)$
-    & 62        & 62        & 62        & 100       & 100       \\
+    & $62^3$        & $62^3$        & $62^3$        & $100^3$       & $100^3$       \\
     \hline
     Precision
     & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} \\
@@ -542,14 +542,14 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} =
     bandwidth (Mbit/s)
     & 50        & 50        & 50        & 50        & 50 \\ %       & 10        & 10 \\
     \hline
-    latency (ms)
-    & 20      & 20      & 20      & 20      & 20 \\ %      & 0.03      & 0.01 \\
-    \hline
+    %latency (ms)
+    %& 20      & 20      & 20      & 20      & 20 \\ %      & 0.03      & 0.01 \\
+    %\hline
     Power (GFlops)
     & 1.5       & 1.5       & 1.5       & 1.5       & 1.5 \\ %      & 1         & 1.5 \\
     \hline
     size $(N)$
-    & 110       & 120       & 130       & 140       & 150  \\ %     & 171       & 171 \\
+    & $110^3$       & $120^3$       & $130^3$       & $140^3$       & $150^3$  \\ %     & 171       & 171 \\
     \hline
     Precision
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5}  & \np{E-5} \\
@@ -662,8 +662,8 @@ the results have given a relative gain more than 2.5, showing the effectiveness
 asynchronous multisplitting  compared to GMRES with two distant clusters.
 
 With these settings, Table~\ref{tab.cluster.2x50} shows
-that after setting the bandwidth of the  inter cluster network to  \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
-of one GFlops, an efficiency of about \np[\%]{40} is
+that after setting the bandwidth of the  inter cluster network to  \np[Mbit/s]{5}, the latency to $20$ millisecond and the processor power
+to one GFlops, an efficiency of about \np[\%]{40} is
 obtained in asynchronous mode for a matrix size of $62^3$ elements. It is noticed that the result remains
 stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
 increasing the matrix size up to $100^3$ elements, it was necessary to increase the
@@ -693,7 +693,7 @@ elements.
 %\CER{Définitivement, les paramètres réseaux variables ici se rapportent au réseau INTER cluster.}
 \section{Conclusion}
 The simulation of the execution of parallel asynchronous iterative algorithms on large scale  clusters has been presented. 
-In this work, we show that SIMGRID is an efficient simulation tool that allows us to 
+In this work, we show that SimGrid is an efficient simulation tool that allows us to 
 reach the following two objectives: 
 
 \begin{enumerate}
@@ -715,7 +715,7 @@ tool to run efficiently an iterative parallel algorithm in asynchronous
 mode in a grid architecture. 
 
 In future works, we plan to extend our experimentations to larger scale platforms by increasing the number of computing cores and the number of clusters. 
-We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to experimentally validate our study.
+We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to better experimentally validate our study. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods.
 
 \section*{Acknowledgment}