X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/9f63b1bdd201bf280fa1aee49eeb60425682273c..9b8612a74b1682a47832402cd8871ab7f7fc9c69:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index 9d20e32..b11da8c 100644
--- a/paper.tex
+++ b/paper.tex
@@ -96,20 +96,21 @@ performed. With some applications, it is difficult, if not impossible, to build
 accurate performance models. That is why another solution is to use a simulation
 tool which allows us to change many parameters of the architecture (network
 bandwidth, latency, number of processors) and to simulate the execution of such
-applications. We have decided to use SimGrid as it enables to benchmark MPI
-applications.
+applications. The main contribution of this paper is to show that the use of a
+simulation tool (here we have decided to use the SimGrid toolkit) can really
+help developpers to better tune their applications for a given multi-core
+architecture.
 
-In this paper, we focus our attention on two parallel iterative algorithms based
+In particular we focus our attention on two parallel iterative algorithms based
 on the  Multisplitting algorithm  and we  compare them  to the  GMRES algorithm.
-These algorithms  are used to  solve libear  systems. Two different  variants of
+These algorithms  are used to  solve linear  systems. Two different  variants of
 the Multisplitting are studied: one  using synchronoous  iterations and  another
-one  with asynchronous iterations. For each algorithm we have  tested different
-parameters to see their influence.  We strongly  recommend people  interested
-by investing  into a  new expensive  hardware  architecture  to   benchmark
-their  applications  using  a simulation tool before.
-
-
-
+one  with asynchronous iterations. For each algorithm we have simulated
+different architecture parameters to evaluate their influence on the overall
+execution time.  The obtain simulated results confirm the real results
+previously obtained on different real multi-core architectures and also confirm
+the efficiency of the asynchronous multisplitting algorithm compared to the
+synchronous GMRES method.
 
 \end{abstract}
 
@@ -390,7 +391,7 @@ until convergence where $h$ is the grid spacing between two adjacent elements in
 
 In the parallel context, the 3D Poisson problem is partitioned into $L\times p$ sub-problems such that $L$ is the number of clusters and $p$ is the number of processors in each cluster. We apply the three-dimensional partitioning instead of the row-by-row one in order to reduce the size of the data shared at the sub-problems boundaries. In this case, each processor is in charge of parallelepipedic block of the problem and has at most six neighbors in the same cluster or in distant clusters with which it shares data at boundaries.
 
-\subsection{Study setup and Simulation Methodology}
+\subsection{Study setup and simulation methodology}
 
 First, to conduct our study, we propose the following methodology
 which can be reused for any grid-enabled applications.\\
@@ -399,10 +400,12 @@ which can be reused for any grid-enabled applications.\\
 the application to be tested. Numerical parallel iterative algorithms
 have been chosen for the study in this paper. \\
 
-\textbf{Step 2}: Collect the software materials needed for the
-experimentation. In our case, we have two variants algorithms for the
-resolution of the 3D-Poisson problem: (1) using the classical GMRES; (2) and the Multisplitting method. In addition, the Simgrid simulator has been chosen to simulate the behaviors of the
-distributed applications. Simgrid is running on the Mesocentre datacenter in the University of  Franche-Comte and also in a virtual machine on a simple laptop. \\
+\textbf{Step 2}: Collect the software materials needed for the experimentation.
+In our case, we have two variants algorithms for the resolution of the
+3D-Poisson problem: (1) using the classical GMRES; (2) and the Multisplitting
+method. In addition, the Simgrid simulator has been chosen to simulate the
+behaviors of the distributed applications. Simgrid is running in a virtual
+machine on a simple laptop. \\
 
 \textbf{Step 3}: Fix the criteria which will be used for the future
 results comparison and analysis. In the scope of this study, we retain
@@ -443,23 +446,20 @@ the program output results:
     capacity" of the network is defined as  the maximum of data that can transit
     from one point to another in a unit of time.
 \item the  network latency  (lat :  microsecond) defined as  the delay  from the
-  start time to send  the data from a source and the  final time the destination
-  have finished to receive it.
+  start time to send  a simple data from a source to a destination.
 \end{enumerate}
-Upon  the   network  characteristics,  another  impacting   factor  is  the
-application dependent volume of data exchanged  between the nodes in the cluster
-and  between distant  clusters.  Large volume  of data  can  be transferred  and
-transit between the clusters and nodes during the code execution.
+Upon  the   network  characteristics,  another  impacting   factor  is  the volume of data exchanged  between the nodes in the cluster
+and  between distant  clusters.  This parameter is application dependent.
 
  In  a grid  environment, it  is common  to distinguish,  on the  one hand,  the
- "intra-network" which refers  to the links between nodes within  a cluster and,
+ "intra-network" which refers  to the links between nodes within  a cluster and
  on  the other  hand, the  "inter-network" which  is the  backbone link  between
- clusters.  In   practice,  these  two   networks  have  different   speeds.  The
- intra-network  generally works  like a  high speed  local network  with a  high
- bandwith and very low latency. In opposite, the inter-network connects clusters
- sometime via  heterogeneous networks components  throuth internet with  a lower
- speed.  The network  between distant  clusters might  be a  bottleneck for  the
- global performance of the application.
+ clusters.  In   practice,  these  two   networks  have  different   speeds.
+ The intra-network  generally works  like a  high speed  local network  with a
+ high bandwith and very low latency. In opposite, the inter-network connects
+ clusters sometime via  heterogeneous networks components  throuth internet with
+ a lower speed.  The network  between distant  clusters might  be a  bottleneck
+ for  the global performance of the application.
 
 \subsection{Comparison of GMRES and Krylov Multisplitting algorithms in synchronous mode}
 
