X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/9db72b31bc5ae56df6c06f94f031aeb35876dd01..3b1a2b4ed67feb9ddc7507280778fb45ed5e2f93:/hpcc.tex?ds=sidebyside

diff --git a/hpcc.tex b/hpcc.tex
index 2e791d7..d9f64af 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -8,7 +8,6 @@
 \usepackage{algpseudocode}
 %\usepackage{amsthm}
 \usepackage{graphicx}
-%\usepackage{xspace}
 \usepackage[american]{babel}
 % Extension pour les liens intra-documents (tagged PDF)
 % et l'affichage correct des URL (commande \url{http://example.com})
@@ -22,12 +21,21 @@
   \renewcommand*\npunitcommand[1]{\text{#1}}
   \npthousandthpartsep{}}
 
+\usepackage{xspace}
+\usepackage[textsize=footnotesize]{todonotes}
+\newcommand{\AG}[2][inline]{%
+  \todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}
+\newcommand{\RC}[2][inline]{%
+  \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace}
+
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
 
 \algnewcommand\algorithmicoutput{\textbf{Output:}}
 \algnewcommand\Output{\item[\algorithmicoutput]}
 
+\newcommand{\MI}{\mathit{MaxIter}}
+
 
 \begin{document}
 
@@ -35,21 +43,23 @@
 
 \author{%
   \IEEEauthorblockN{%
-    Raphaël Couturier,
-    Arnaud Giersch,
+    Charles Emile Ramamonjisoa and
     David Laiymani and
-    Charles Emile Ramamonjisoa
+    Arnaud Giersch and
+    Lilia Ziane Khodja and
+    Raphaël Couturier
   }
   \IEEEauthorblockA{%
     Femto-ST Institute - DISC Department\\
     Université de Franche-Comté\\
     Belfort\\
-    Email: \email{raphael.couturier@univ-fcomte.fr}
+    Email: \email{{raphael.couturier,arnaud.giersch,david.laiymani,charles.ramamonjisoa}@univ-fcomte.fr}
   }
 }
 
 \maketitle
 
+\RC{Ordre des autheurs pas définitif.\\ Adresse de Lilia: Inria Bordeaux Sud-Ouest, 200 Avenue de la Vieille Tour, 33405 Talence Cedex, France \\ Email: lilia.ziane@inria.fr}
 \begin{abstract}
 The abstract goes here.
 \end{abstract}
@@ -71,13 +81,13 @@ iterations ($X_{n +1} = f(X_{n})$) from an initial value $X_{0}$ to find
 an approximate value $X^*$ of the solution with a very low
 residual error. Several well-known methods demonstrate the convergence 
 of these algorithms. Generally, to reduce the complexity and the 
-execution time, the problem is divided into several "pieces" that will 
+execution time, the problem is divided into several \emph{pieces} that will
 be solved in parallel on multiple processing units. The latter will 
 communicate each intermediate results before a new iteration starts 
 until the approximate solution is reached. These distributed parallel 
-computations can be performed either in "synchronous" communication mode 
+computations can be performed either in \emph{synchronous} communication mode
 where a new iteration begin only when all nodes communications are 
-completed, either "asynchronous" mode where processors can continue 
+completed, either \emph{asynchronous} mode where processors can continue
 independently without or few synchronization points. Despite the 
 effectiveness of iterative approach, a major drawback of the method is 
 the requirement of huge resources in terms of computing capacity, 
@@ -128,7 +138,7 @@ Décrire le modèle asynchrone. Je m'en charge (DL)
 
 \section{SimGrid}
 
-Décrire SimGrid (Arnaud)
+Décrire SimGrid~\cite{casanova+legrand+quinson.2008.simgrid} (Arnaud)
 
 
 
@@ -139,7 +149,7 @@ Décrire SimGrid (Arnaud)
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 %Décrire le problème (algo) traité ainsi que le processus d'adaptation à SimGrid.
-Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $y$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi partitioning to solve this linear system on a large scale platform composed of $L$ clusters of processors. In this case, we apply a row-by-row splitting without overlapping  
+Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $b$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi splitting to solve this linear system on a large scale platform composed of $L$ clusters of processors. In this case, we apply a row-by-row splitting without overlapping  
 \[
 \left(\begin{array}{ccc}
 A_{11} & \cdots & A_{1L} \\
@@ -154,47 +164,63 @@ X_L
 \end{array} \right)
 =
 \left(\begin{array}{c}
-Y_1 \\
+B_1 \\
 \vdots\\
-Y_L
+B_L
 \end{array} \right)\] 
-in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,i\in\{1,\ldots,L\}$ $A_{li}$ is a rectangular block of $A$ of size $n_l\times n_i$, $X_l$ and $Y_l$ are sub-vectors of $x$ and $y$, respectively, each of size $n_l$ and $\sum_{l} n_l=\sum_{i} n_i=n$.
+in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,m\in\{1,\ldots,L\}$ $A_{lm}$ is a rectangular block of $A$ of size $n_l\times n_m$, $X_l$ and $B_l$ are sub-vectors of $x$ and $b$, respectively, each of size $n_l$ and $\sum_{l} n_l=\sum_{m} n_m=n$.
 
-The multisplitting method proceeds by iteration to solve in parallel the linear system by $L$ clusters of processors, in such a way each sub-system
+The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}
 \left\{
 \begin{array}{l}
 A_{ll}X_l = Y_l \mbox{,~such that}\\
-Y_l = B_l - \displaystyle\sum_{i=1,i\neq l}^{L}A_{li}X_i,
+Y_l = B_l - \displaystyle\sum_{\substack{m=1\\ m\neq l}}^{L}A_{lm}X_m
 \end{array}
 \right.
 \label{eq:4.1}
 \end{equation}
-is solved independently by a cluster and communication are required to update the right-hand side sub-vectors $Y_l$, such that the sub-vectors $X_i$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers. 
+is solved independently by a cluster and communication are required to update the right-hand side sub-vector $Y_l$, such that the sub-vectors $X_m$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers. 
 
 \begin{algorithm}
-\caption{A multisplitting solver with inner iteration GMRES method}
+\caption{A multisplitting solver with GMRES method}
 \begin{algorithmic}[1]
-\Input $A_l$ (local sparse matrix), $B_l$ (local right-hand side), $x^0$ (initial guess)
-\Output $X_l$ (local solution vector)\vspace{0.2cm}
-\State Load $A_l$, $B_l$, $x^0$
-\State Initialize the shared vector $\hat{x}=x^0$
-\For {$k=1,2,3,\ldots$ until the global convergence}
-\State $x^0=\hat{x}$
-\State Inner iteration solver: \Call{InnerSolver}{$x^0$, $k$}
-\State Exchange the local solution ${X}_l^k$ with the neighboring clusters and copy the shared vector elements in $\hat{x}$
+\Input $A_l$ (sparse sub-matrix), $B_l$ (right-hand side sub-vector)
+\Output $X_l$ (solution sub-vector)\vspace{0.2cm}
+\State Load $A_l$, $B_l$
+\State Initialize the solution vector $x^0$
+\For {$k=0,1,2,\ldots$ until the global convergence}
+\State Restart outer iteration with $x^0=x^k$
+\State Inner iteration: \Call{InnerSolver}{$x^0$, $k+1$}
+\State Send shared elements of $X_l^{k+1}$ to neighboring clusters
+\State Receive shared elements in $\{X_m^{k+1}\}_{m\neq l}$
 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $k$}
-\State Compute the local right-hand side: $Y_l = B_l - \sum^L_{i=1,i\neq l}A_{li}X_i^0$
-\State Solving the local splitting $A_{ll}X_l^k=Y_l$ using the parallel GMRES method, such that $X_l^0$ is the local initial guess
+\State Compute local right-hand side $Y_l$: \[Y_l = B_l - \sum\nolimits^L_{\substack{m=1 \\m\neq l}}A_{lm}X_m^0\]
+\State Solving sub-system $A_{ll}X_l^k=Y_l$ with the parallel GMRES method
 \State \Return $X_l^k$
 \EndFunction
 \end{algorithmic}
 \label{algo:01}
 \end{algorithm}
+
+Algorithm~\ref{algo:01} shows the main key points of the multisplitting method to solve a large sparse linear system. This algorithm is based on an outer-inner iteration method where the parallel synchronous GMRES method is used to solve the inner iteration. It is executed in parallel by each cluster of processors. For all $l,m\in\{1,\ldots,L\}$, the matrices and vectors with the subscript $l$ represent the local data for cluster $l$, while $\{A_{lm}\}_{m\neq l}$ are off-diagonal matrices of sparse matrix $A$ and $\{X_m\}_{m\neq l}$ contain vector elements of solution $x$ shared with neighboring clusters. At every outer iteration $k$, asynchronous communications are performed between processors of the local cluster and those of distant clusters (lines $6$ and $7$ in Algorithm~\ref{algo:01}). The shared vector elements of the solution $x$ are exchanged by message passing using MPI non-blocking communication routines. 
+
+\begin{figure}
+\centering
+  \includegraphics[width=60mm,keepaspectratio]{clustering}
+\caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.}
+\label{fig:4.1}
+\end{figure}
+
+The global convergence of the asynchronous multisplitting solver is detected when the clusters of processors have all converged locally. We implemented the global convergence detection process as follows. On each cluster a master processor is designated (for example the processor with rank $1$) and masters of all clusters are interconnected by a virtual unidirectional ring network (see Figure~\ref{fig:4.1}). During the resolution, a Boolean token circulates around the virtual ring from a master processor to another until the global convergence is achieved. So starting from the cluster with rank $1$, each master processor $i$ sets the token to {\it True} if the local convergence is achieved or to {\it False} otherwise, and sends it to master processor $i+1$. Finally, the global convergence is detected when the master of cluster $1$ receive from the master of cluster $L$ a token set to {\it True}. In this case, the master of cluster $1$ sends a stop message to masters of other clusters. In this work, the local convergence on each cluster $l$ is detected when the following condition is satisfied
+\[(k\leq \MI) \mbox{~or~} (\|X_l^k - X_l^{k+1}\|_{\infty}\leq\epsilon)\]
+where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the tolerance threshold of the error computed between two successive local solution $X_l^k$ and $X_l^{k+1}$. 
+
+\RC{Description du processus d'adaptation de l'algo multisplitting à SimGrid}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
@@ -224,7 +250,7 @@ A priori, obtaining a speedup less than 1 would be difficult in a local area
 network configuration where the synchronous mode will take advantage on the rapid
 exchange of information on such high-speed links. Thus, the methodology adopted
 was to launch the application on clustered network. In this last configuration,
-degrading the inter-cluster network performance will "penalize" the synchronous
+degrading the inter-cluster network performance will \emph{penalize} the synchronous
 mode allowing to get a speedup lower than 1. This action simulates the case of
 clusters linked with long distance network like Internet.
 
@@ -270,6 +296,7 @@ lat latency, \dots{}).
   \centering
   \caption{2 clusters X 50 nodes}
   \label{tab.cluster.2x50}
+  \AG{Les images manquent dans le dépôt Git. Si ce sont vraiment des tableaux, utiliser un format vectoriel (eps ou pdf), et surtout pas de jpeg!}
   \includegraphics[width=209pt]{img1.jpg}
 \end{table}
 
@@ -277,6 +304,7 @@ lat latency, \dots{}).
   \centering
   \caption{3 clusters X 33 nodes}
   \label{tab.cluster.3x33}
+  \AG{Le fichier manque.}
   \includegraphics[width=209pt]{img2.jpg}
 \end{table}
 
@@ -284,6 +312,7 @@ lat latency, \dots{}).
   \centering
   \caption{3 clusters X 67 nodes}
   \label{tab.cluster.3x67}
+  \AG{Le fichier manque.}
 %  \includegraphics[width=160pt]{img3.jpg}
   \includegraphics[scale=0.5]{img3.jpg}
 \end{table}