corrections

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index cb85b0063b8e26656f49987a2ce027d70c260ef2..622a2f08df39317112c46e6ff659eff8c24bb411 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -483,7 +483,7 @@ The ratio between the simulated execution time of synchronous GMRES algorithm
 compared to the asynchronous multisplitting algorithm ($t_\text{GMRES} / t_\text{Multisplitting}$) is defined as the \emph{relative gain}. So,
 our objective running the algorithm in SimGrid is to obtain a relative gain greater than 1.
 A priori, obtaining a relative gain greater than 1 would be difficult in a local
 compared to the asynchronous multisplitting algorithm ($t_\text{GMRES} / t_\text{Multisplitting}$) is defined as the \emph{relative gain}. So,
 our objective running the algorithm in SimGrid is to obtain a relative gain greater than 1.
 A priori, obtaining a relative gain greater than 1 would be difficult in a local

-area network configuration where the synchronous mode will take advantage on the
+area network configuration where the synchronous GMRES method will take advantage on the

 rapid exchange of information on such high-speed links. Thus, the methodology
 adopted was to launch the application on a clustered network. In this
 configuration, degrading the inter-cluster network performance will penalize the
 rapid exchange of information on such high-speed links. Thus, the methodology
 adopted was to launch the application on a clustered network. In this
 configuration, degrading the inter-cluster network performance will penalize the


@@ -660,7 +660,7 @@ the results have given a relative gain more than 2.5, showing the effectiveness
 asynchronous multiplsitting  compared to GMRES with two distant clusters.
 
 With these settings, Table~\ref{tab.cluster.2x50} shows
 asynchronous multiplsitting  compared to GMRES with two distant clusters.
 
 With these settings, Table~\ref{tab.cluster.2x50} shows

-that after a deterioration of inter cluster network with a bandwidth of \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
+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
 obtained in asynchronous mode for a matrix size of 62 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
 of one GFlops, an efficiency of about \np[\%]{40} is
 obtained in asynchronous mode for a matrix size of 62 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


@@ -707,17 +707,18 @@ of the cluster and network specifications permitting to save time in
 executing the algorithm in asynchronous mode.
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 
 executing the algorithm in asynchronous mode.
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 

-speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
+speeder up to \np[\%]{40} comparing to the synchronous GMRES method

 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 
 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 

- Several studies have already addressed the performance execution time of 
+Several studies have already addressed the performance execution time of 

 this class of algorithm. The work presented in this paper has 
 demonstrated an original solution to optimize the use of a simulation 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 
 this class of algorithm. The work presented in this paper has 
 demonstrated an original solution to optimize the use of a simulation 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 

-\LZK{Perspectives???}
+For our futur 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.

 
 \section*{Acknowledgment}
 
 
 \section*{Acknowledgment}