-\documentclass[conference]{IEEEtran}
+\documentclass[times]{cpeauth}
+
+\usepackage{moreverb}
+
+%\usepackage[dvips,colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref}
+
+%\newcommand\BibTeX{{\rmfamily B\kern-.05em \textsc{i\kern-.025em b}\kern-.08em
+%T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
+
+\def\volumeyear{2015}
+
+\usepackage{graphicx}
+\usepackage{wrapfig}
+\usepackage{grffile}
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\usepackage[textsize=footnotesize]{todonotes}
+
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+\newcommand{\DL}[2][inline]{%
+ \todo[color=pink!10,#1]{\sffamily\textbf{DL:} #2}\xspace}
\algnewcommand\algorithmicinput{\textbf{Input:}}
\algnewcommand\Input{\item[\algorithmicinput]}
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-\newcommand{\MI}{\mathit{MaxIter}}
+\newcommand{\TOLG}{\mathit{tol_{gmres}}}
+\newcommand{\MIG}{\mathit{maxit_{gmres}}}
+\newcommand{\TOLM}{\mathit{tol_{multi}}}
+\newcommand{\MIM}{\mathit{maxit_{multi}}}
+\newcommand{\TOLC}{\mathit{tol_{cgls}}}
+\newcommand{\MIC}{\mathit{maxit_{cgls}}}
\usepackage{array}
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\definecolor{Gray}{gray}{0.9}
-\begin{document}
-\RCE{Titre a confirmer.}
-\title{Comparative performance analysis of simulated grid-enabled numerical iterative algorithms}
-\author{%
- \IEEEauthorblockN{%
- Charles Emile Ramamonjisoa and
+
+\begin{document} \RCE{Titre a confirmer.} \title{Comparative performance
+analysis of simulated grid-enabled numerical iterative algorithms}
+%\itshape{\journalnamelc}\footnotemark[2]}
+
+\author{ Charles Emile Ramamonjisoa and
David Laiymani and
Arnaud Giersch and
Lilia Ziane Khodja and
Raphaël Couturier
- }
- \IEEEauthorblockA{%
+}
+
+\address{
+ \centering
Femto-ST Institute - DISC Department\\
Université de Franche-Comté\\
Belfort\\
Email: \email{{raphael.couturier,arnaud.giersch,david.laiymani,charles.ramamonjisoa}@univ-fcomte.fr}
- }
}
-\maketitle
+%% Lilia Ziane Khodja: Department of Aerospace \& Mechanical Engineering\\ Non Linear Computational Mechanics\\ University of Liege\\ Liege, Belgium. Email: l.zianekhodja@ulg.ac.be
+
+\begin{abstract} The behavior of multi-core applications is always a challenge
+to predict, especially with a new architecture for which no experiment has been
+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. 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 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 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 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.
-\begin{abstract}
-ABSTRACT
+\end{abstract}
+%\keywords{Algorithm; distributed; iterative; asynchronous; simulation; simgrid;
+%performance}
+\keywords{ Performance evaluation, Simulation, SimGrid, Synchronous and asynchronous iterations, Multisplitting algorithms}
-Keywords : Algorithm distributed iterative asynchronous simulation simgrid performance
+\maketitle
-\end{abstract}
+\section{Introduction} The use of multi-core architectures to solve large
+scientific problems seems to become imperative in many situations.
+Whatever the scale of these architectures (distributed clusters, computational
+grids, embedded multi-core,~\ldots) they are generally well adapted to execute
+complex parallel applications operating on a large amount of data.
+Unfortunately, users (industrials or scientists), who need such computational
+resources, may not have an easy access to such efficient architectures. The cost
+of using the platform and/or the cost of testing and deploying an application
+are often very important. So, in this context it is difficult to optimize a
+given application for a given architecture. In this way and in order to reduce
+the access cost to these computing resources it seems very interesting to use a
+simulation environment. The advantages are numerous: development life cycle,
+code debugging, ability to obtain results quickly~\ldots. In counterpart, the simulation results need to be consistent with the real ones.
+
+In this paper we focus on a class of highly efficient parallel algorithms called
+\emph{iterative algorithms}. The parallel scheme of iterative methods is quite
+simple. It generally involves the division of the problem into several
+\emph{blocks} that will be solved in parallel on multiple processing
+units. Each processing unit has to compute an iteration to send/receive some
+data dependencies to/from its neighbors and to iterate this process until the
+convergence of the method. Several well-known studies demonstrate the
+convergence of these algorithms~\cite{BT89,bahi07}. In this processing mode a
+task cannot begin a new iteration while it has not received data dependencies
+from its neighbors. We say that the iteration computation follows a
+\textit{synchronous} scheme. In the asynchronous scheme a task can compute a new
+iteration without having to wait for the data dependencies coming from its
+neighbors. Both communication and computations are \textit{asynchronous}
+inducing that there is no more idle time, due to synchronizations, between two
+iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks
+that we detail in section~\ref{sec:asynchro} but even if the number of
+iterations required to converge is generally greater than for the synchronous
+case, it appears that the asynchronous iterative scheme can significantly
+reduce overall execution times by suppressing idle times due to
+synchronizations~(see~\cite{bahi07} for more details).
+
+Nevertheless, in both cases (synchronous or asynchronous) it is very time
+consuming to find optimal configuration and deployment requirements for a given
+application on a given multi-core architecture. Finding good resource
+allocations policies under varying CPU power, network speeds and loads is very
+challenging and labor intensive~\cite{Calheiros:2011:CTM:1951445.1951450}. This
+problematic is even more difficult for the asynchronous scheme where a small
+parameter variation of the execution platform can lead to very different numbers
+of iterations to reach the converge and so to very different execution times. In
+this challenging context we think that the use of a simulation tool can greatly
+leverage the possibility of testing various platform scenarios.
+
+The main contribution of this paper is to show that the use of a simulation tool
+(i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real parallel
+applications (i.e. large linear system solvers) can help developers to better
+tune their application for a given multi-core architecture. To show the validity
+of this approach we first compare the simulated execution of the multisplitting
+algorithm with the GMRES (Generalized Minimal Residual)
+solver~\cite{saad86} in synchronous mode. The obtained results on different
+simulated multi-core architectures confirm the real results previously obtained
+on non simulated architectures. We also confirm the efficiency of the
+asynchronous multisplitting algorithm compared to the synchronous GMRES. In
+this way and with a simple computing architecture (a laptop) SimGrid allows us
+to run a test campaign of a real parallel iterative applications on
+different simulated multi-core architectures. To our knowledge, there is no
+related work on the large-scale multi-core simulation of a real synchronous and
+asynchronous iterative application.
+
+This paper is organized as follows. Section~\ref{sec:asynchro} presents the
+iteration model we use and more particularly the asynchronous scheme. In
+section~\ref{sec:simgrid} the SimGrid simulation toolkit is presented.
+Section~\ref{sec:04} details the different solvers that we use. Finally our
+experimental results are presented in section~\ref{sec:expe} followed by some
+concluding remarks and perspectives.
-\section{Introduction}
\section{The asynchronous iteration model}
+\label{sec:asynchro}
+
+Asynchronous iterative methods have been studied for many years theoritecally and
+practically. Many methods have been considered and convergence results have been
+proved. These methods can be used to solve, in parallel, fixed point problems
+(i.e. problems for which the solution is $x^\star =f(x^\star)$. In practice,
+asynchronous iterations methods can be used to solve, for example, linear and
+non-linear systems of equations or optimization problems, interested readers are
+invited to read~\cite{BT89,bahi07}.
+
+Before using an asynchronous iterative method, the convergence must be
+studied. Otherwise, the application is not ensure to reach the convergence. An
+algorithm that supports both the synchronous or the asynchronous iteration model
+requires very few modifications to be able to be executed in both variants. In
+practice, only the communications and convergence detection are different. In
+the synchronous mode, iterations are synchronized whereas in the asynchronous
+one, they are not. It should be noticed that non blocking communications can be
+used in both modes. Concerning the convergence detection, synchronous variants
+can use a global convergence procedure which acts as a global synchronization
+point. In the asynchronous model, the convergence detection is more tricky as
+it must not synchronize all the processors. Interested readers can
+consult~\cite{myBCCV05c,bahi07,ccl09:ij}.
\section{SimGrid}
+ \label{sec:simgrid}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\section{Two-stage multisplitting methods}
+\label{sec:04}
+\subsection{Synchronous and asynchronous two-stage methods for sparse linear systems}
+\label{sec:04.01}
+In this paper we focus on two-stage multisplitting methods in their both versions (synchronous and asynchronous)~\cite{Frommer92,Szyld92,Bru95}. These iterative methods are based on multisplitting methods~\cite{O'leary85,White86,Alefeld97} and use two nested iterations: the outer iteration and the inner iteration. Let us consider the following sparse linear system of $n$ equations in $\mathbb{R}$:
+\begin{equation}
+Ax=b,
+\label{eq:01}
+\end{equation}
+where $A$ is a sparse square and nonsingular matrix, $b$ is the right-hand side and $x$ is the solution of the system. Our work in this paper is restricted to the block Jacobi splitting method. This approach of multisplitting consists in partitioning the matrix $A$ into $L$ horizontal band matrices of order $\frac{n}{L}\times n$ without overlapping (i.e. sub-vectors $\{x_\ell\}_{1\leq\ell\leq L}$ are disjoint). Two-stage multisplitting methods solve the linear system~(\ref{eq:01}) iteratively as follows:
+\begin{equation}
+x_\ell^{k+1} = A_{\ell\ell}^{-1}(b_\ell - \displaystyle\sum^{L}_{\substack{m=1\\m\neq\ell}}{A_{\ell m}x^k_m}),\mbox{~for~}\ell=1,\ldots,L\mbox{~and~}k=1,2,3,\ldots
+\label{eq:02}
+\end{equation}
+where $x_\ell$ are sub-vectors of the solution $x$, $b_\ell$ are the sub-vectors of the right-hand side $b$, and $A_{\ell\ell}$ and $A_{\ell m}$ are diagonal and off-diagonal blocks of matrix $A$ respectively. The iterations of these methods can naturally be computed in parallel such that each processor or cluster of processors is responsible for solving one splitting as a linear sub-system:
+\begin{equation}
+A_{\ell\ell} x_\ell = c_\ell,\mbox{~for~}\ell=1,\ldots,L,
+\label{eq:03}
+\end{equation}
+where right-hand sides $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m$ are computed using the shared vectors $x_m$. In this paper, we use the well-known iterative method GMRES ({\it Generalized Minimal RESidual})~\cite{saad86} as an inner iteration to approximate the solutions of the different splittings arising from the block Jacobi multisplitting of matrix $A$. The algorithm in Figure~\ref{01} shows the main key points of our block Jacobi two-stage method executed by a cluster of processors. In line~\ref{solve}, the linear sub-system~(\ref{eq:03}) is solved in parallel using GMRES method where $\MIG$ and $\TOLG$ are the maximum number of inner iterations and the tolerance threshold for GMRES respectively. The convergence of the two-stage multisplitting methods, based on synchronous or asynchronous iterations, has been studied by many authors for example~\cite{Bru95,bahi07}.
+
+\begin{figure}[t]
+%\begin{algorithm}[t]
+%\caption{Block Jacobi two-stage multisplitting method}
+\begin{algorithmic}[1]
+ \Input $A_\ell$ (sparse matrix), $b_\ell$ (right-hand side)
+ \Output $x_\ell$ (solution vector)\vspace{0.2cm}
+ \State Set the initial guess $x^0$
+ \For {$k=1,2,3,\ldots$ until convergence}
+ \State $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m^{k-1}$
+ \State $x^k_\ell=Solve_{gmres}(A_{\ell\ell},c_\ell,x^{k-1}_\ell,\MIG,\TOLG)$\label{solve}
+ \State Send $x_\ell^k$ to neighboring clusters\label{send}
+ \State Receive $\{x_m^k\}_{m\neq\ell}$ from neighboring clusters\label{recv}
+ \EndFor
+\end{algorithmic}
+\caption{Block Jacobi two-stage multisplitting method}
+\label{alg:01}
+%\end{algorithm}
+\end{figure}
+
+In this paper, we propose two algorithms of two-stage multisplitting methods. The first algorithm is based on the asynchronous model which allows communications to be overlapped by computations and reduces the idle times resulting from the synchronizations. So in the asynchronous mode, our two-stage algorithm uses asynchronous outer iterations and asynchronous communications between clusters. The communications (i.e. lines~\ref{send} and~\ref{recv} in Figure~\ref{alg:01}) are performed by message passing using MPI non-blocking communication routines. The convergence of the asynchronous iterations is detected when all clusters have locally converged:
+\begin{equation}
+k\geq\MIM\mbox{~or~}\|x_\ell^{k+1}-x_\ell^k\|_{\infty }\leq\TOLM,
+\label{eq:04}
+\end{equation}
+where $\MIM$ is the maximum number of outer iterations and $\TOLM$ is the tolerance threshold for the two-stage algorithm.
+
+The second two-stage algorithm is based on synchronous outer iterations. We propose to use the Krylov iteration based on residual minimization to improve the slow convergence of the multisplitting methods. In this case, a $n\times s$ matrix $S$ is set using solutions issued from the inner iteration:
+\begin{equation}
+S=[x^1,x^2,\ldots,x^s],~s\ll n.
+\label{eq:05}
+\end{equation}
+At each $s$ outer iterations, the algorithm computes a new approximation $\tilde{x}=S\alpha$ which minimizes the residual:
+\begin{equation}
+\min_{\alpha\in\mathbb{R}^s}{\|b-AS\alpha\|_2}.
+\label{eq:06}
+\end{equation}
+The algorithm in Figure~\ref{alg:02} includes the procedure of the residual minimization and the outer iteration is restarted with a new approximation $\tilde{x}$ at every $s$ iterations. The least-squares problem~(\ref{eq:06}) is solved in parallel by all clusters using CGLS method~\cite{Hestenes52} such that $\MIC$ is the maximum number of iterations and $\TOLC$ is the tolerance threshold for this method (line~\ref{cgls} in Figure~\ref{alg:02}).
+
+\begin{figure}[t]
+%\begin{algorithm}[t]
+%\caption{Krylov two-stage method using block Jacobi multisplitting}
+\begin{algorithmic}[1]
+ \Input $A_\ell$ (sparse matrix), $b_\ell$ (right-hand side)
+ \Output $x_\ell$ (solution vector)\vspace{0.2cm}
+ \State Set the initial guess $x^0$
+ \For {$k=1,2,3,\ldots$ until convergence}
+ \State $c_\ell=b_\ell-\sum_{m\neq\ell}A_{\ell m}x_m^{k-1}$
+ \State $x^k_\ell=Solve_{gmres}(A_{\ell\ell},c_\ell,x^{k-1}_\ell,\MIG,\TOLG)$
+ \State $S_{\ell,k\mod s}=x_\ell^k$
+ \If{$k\mod s = 0$}
+ \State $\alpha = Solve_{cgls}(AS,b,\MIC,\TOLC)$\label{cgls}
+ \State $\tilde{x_\ell}=S_\ell\alpha$
+ \State Send $\tilde{x_\ell}$ to neighboring clusters
+ \Else
+ \State Send $x_\ell^k$ to neighboring clusters
+ \EndIf
+ \State Receive $\{x_m^k\}_{m\neq\ell}$ from neighboring clusters
+ \EndFor
+\end{algorithmic}
+\caption{Krylov two-stage method using block Jacobi multisplitting}
+\label{alg:02}
+%\end{algorithm}
+\end{figure}
+
+\subsection{Simulation of the two-stage methods using SimGrid toolkit}
+\label{sec:04.02}
+
+One of our objectives when simulating the application in Simgrid is, as in real
+life, to get accurate results (solutions of the problem) but also to ensure the
+test reproducibility under the same conditions. According to our experience,
+very few modifications are required to adapt a MPI program for the Simgrid
+simulator using SMPI (Simulator MPI). The first modification is to include SMPI
+libraries and related header files (smpi.h). The second modification is to
+suppress all global variables by replacing them with local variables or using a
+Simgrid selector called "runtime automatic switching"
+(smpi/privatize\_global\_variables). Indeed, global variables can generate side
+effects on runtime between the threads running in the same process and generated by
+Simgrid to simulate the grid environment.
+
+%\RC{On vire cette phrase ?} \RCE {Si c'est la phrase d'avant sur les threads, je pense qu'on peut la retenir car c'est l'explication du pourquoi Simgrid n'aime pas les variables globales. Si c'est pas bien dit, on peut la reformuler. Si c'est la phrase ci-apres, effectivement, on peut la virer si elle preterais a discussion}The
+%last modification on the MPI program pointed out for some cases, the review of
+%the sequence of the MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions which
+%might cause an infinite loop.
+
+
+\paragraph{Simgrid Simulator parameters}
+\ \\ \noindent Before running a Simgrid benchmark, many parameters for the
+computation platform must be defined. For our experiments, we consider platforms
+in which several clusters are geographically distant, so there are intra and
+inter-cluster communications. In the following, these parameters are described:
+
+\begin{itemize}
+ \item hostfile: hosts description file.
+ \item platform: file describing the platform architecture: clusters (CPU power,
+\dots{}), intra cluster network description, inter cluster network (bandwidth bw,
+latency lat, \dots{}).
+ \item archi : grid computational description (number of clusters, number of
+nodes/processors for each cluster).
+\end{itemize}
+\noindent
+In addition, the following arguments are given to the programs at runtime:
+
+\begin{itemize}
+ \item maximum number of inner iterations $\MIG$ and outer iterations $\MIM$,
+ \item inner precision $\TOLG$ and outer precision $\TOLM$,
+ \item matrix sizes of the 3D Poisson problem: N$_{x}$, N$_{y}$ and N$_{z}$ on axis $x$, $y$ and $z$ respectively,
+ \item matrix diagonal value is fixed to $6.0$ for synchronous Krylov multisplitting experiments and $6.2$ for asynchronous block Jacobi experiments,
+ \item matrix off-diagonal value is fixed to $-1.0$,
+ \item number of vectors in matrix $S$ (i.e. value of $s$),
+ \item maximum number of iterations $\MIC$ and precision $\TOLC$ for CGLS method,
+ \item maximum number of iterations and precision for the classical GMRES method,
+ \item maximum number of restarts for the Arnorldi process in GMRES method,
+ \item execution mode: synchronous or asynchronous.
+\end{itemize}
+\LZK{CE pourrais tu vérifier et confirmer les valeurs des éléments diag et off-diag de la matrice?}
+\RCE{oui, les valeurs de diag et off-diag donnees sont ok}
+
+It should also be noticed that both solvers have been executed with the Simgrid selector \texttt{-cfg=smpi/running\_power} which determines the computational power (here 19GFlops) of the simulator host machine.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\section{Experimental Results}
+\label{sec:expe}
+
+In this section, experiments for both Multisplitting algorithms are reported. First the 3D Poisson problem used in our experiments is described.
+
+\subsection{The 3D Poisson problem}
+
+
+We use our two-stage algorithms to solve the well-known Poisson problem $\nabla^2\phi=f$~\cite{Polyanin01}. In three-dimensional Cartesian coordinates in $\mathbb{R}^3$, the problem takes the following form:
+\begin{equation}
+\frac{\partial^2}{\partial x^2}\phi(x,y,z)+\frac{\partial^2}{\partial y^2}\phi(x,y,z)+\frac{\partial^2}{\partial z^2}\phi(x,y,z)=f(x,y,z)\mbox{~in the domain~}\Omega
+\label{eq:07}
+\end{equation}
+such that:
+\begin{equation*}
+\phi(x,y,z)=0\mbox{~on the boundary~}\partial\Omega
+\end{equation*}
+where the real-valued function $\phi(x,y,z)$ is the solution sought, $f(x,y,z)$ is a known function and $\Omega=[0,1]^3$. The 3D discretization of the Laplace operator $\nabla^2$ with the finite difference scheme includes 7 points stencil on the computational grid. The numerical approximation of the Poisson problem on three-dimensional grid is repeatedly computed as $\phi=\phi^\star$ such that:
+\begin{equation}
+\begin{array}{ll}
+\phi^\star(x,y,z)=&\frac{1}{6}(\phi(x-h,y,z)+\phi(x,y-h,z)+\phi(x,y,z-h)\\&+\phi(x+h,y,z)+\phi(x,y+h,z)+\phi(x,y,z+h)\\&-h^2f(x,y,z))
+\end{array}
+\label{eq:08}
+\end{equation}
+until convergence where $h$ is the grid spacing between two adjacent elements in the 3D computational grid.
+
+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}
+
+First, to conduct our study, we propose the following methodology
+which can be reused for any grid-enabled applications.\\
+
+\textbf{Step 1}: Choose with the end users the class of algorithms or
+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 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
+on the one hand the algorithm execution mode (synchronous and asynchronous)
+and on the other hand the execution time and the number of iterations to reach the convergence. \\
+
+\textbf{Step 4 }: Set up the different grid testbed environments that will be
+simulated in the simulator tool to run the program. The following architecture
+has been configured in Simgrid : 2x16, 4x8, 4x16, 8x8 and 2x50. The first number
+represents the number of clusters in the grid and the second number represents
+the number of hosts (processors/cores) in each cluster. The network has been
+designed to operate with a bandwidth equals to 10Gbits (resp. 1Gbits/s) and a
+latency of 8.10$^{-6}$ seconds (resp. 5.10$^{-5}$) for the intra-clusters links
+(resp. inter-clusters backbone links). \\
+
+\textbf{Step 5}: Conduct an extensive and comprehensive testings
+within these configurations by varying the key parameters, especially
+the CPU power capacity, the network parameters and also the size of the
+input data. \\
+
+\textbf{Step 6} : Collect and analyze the output results.
+
+\subsection{Factors impacting distributed applications performance in
+a grid environment}
+
+When running a distributed application in a computational grid, many factors may
+have a strong impact on the performance. First of all, the architecture of the
+grid itself can obviously influence the performance results of the program. The
+performance gain might be important theoretically when the number of clusters
+and/or the number of nodes (processors/cores) in each individual cluster
+increase.
+
+Another important factor impacting the overall performance of the application
+is the network configuration. Two main network parameters can modify drastically
+the program output results:
+\begin{enumerate}
+\item the network bandwidth (bw=bits/s) also known as "the data-carrying
+ 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 a simple data from a source to a destination.
+\end{enumerate}
+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
+ 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.
+
+\subsection{Comparison of GMRES and Krylov Multisplitting algorithms in synchronous mode}
+
+In the scope of this paper, our first objective is to analyze when the Krylov
+Multisplitting method has better performance than the classical GMRES
+method. With a synchronous iterative method, better performance mean a
+smaller number of iterations and execution time before reaching the convergence.
+For a systematic study, the experiments should figure out that, for various
+grid parameters values, the simulator will confirm the targeted outcomes,
+particularly for poor and slow networks, focusing on the impact on the
+communication performance on the chosen class of algorithm.
+
+The following paragraphs present the test conditions, the output results
+and our comments.\\
+
+
+\subsubsection{Execution of the algorithms on various computational grid
+architectures and scaling up the input matrix size}
+\ \\
+% environment
+
+\begin{table} [ht!]
+\begin{center}
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 2x16, 4x8, 4x16 and 8x8\\ %\hline
+ Network & N2 : bw=1Gbits/s - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ %\hline
+ - & N$_{x}$ x N$_{y}$ x N$_{z}$ =170 x 170 x 170 \\ \hline
+ \end{tabular}
+\caption{Test conditions: Various grid configurations with the input matix size N$_{x}$=150 or N$_{x}$=170 \RC{je ne comprends pas la légende... Ca ne serait pas plutot Characteristics of cluster (mais il faudrait lui donner un nom)}}
+\label{tab:01}
+\end{center}
+\end{table}
+
+
+
+
+%\RCE{J'ai voulu mettre les tableaux des données mais je pense que c'est inutile et ça va surcharger}
+
+
+In this section, we analyze the performance of algorithms running on various
+grid configurations (2x16, 4x8, 4x16 and 8x8). First, the results in Figure~\ref{fig:01}
+show for all grid configurations the non-variation of the number of iterations of
+classical GMRES for a given input matrix size; it is not the case for the
+multisplitting method.
+
+\RC{CE attention tu n'as pas mis de label dans tes figures, donc c'est le bordel, j'en mets mais vérifie...}
+\RC{Les légendes ne sont pas explicites...}
+
+
+\begin{figure} [ht!]
+ \begin{center}
+ \includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf}
+ \end{center}
+ \caption{Various grid configurations with the input matrix size N$_{x}$=150 and N$_{x}$=170}
+ \label{fig:01}
+\end{figure}
+
+
+The execution times between the two algorithms is significant with different
+grid architectures, even with the same number of processors (for example, 2x16
+and 4x8). We can observ the low sensitivity of the Krylov multisplitting method
+(compared with the classical GMRES) when scaling up the number of the processors
+in the grid: in average, the GMRES (resp. Multisplitting) algorithm performs
+$40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
+
+\subsubsection{Running on two different inter-clusters network speeds \\}
+
+\begin{table} [ht!]
+\begin{center}
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 2x16, 4x8\\ %\hline
+ Network & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\hline
+ - & N2 : bw=1Gbs-lat=5.10$^{-5}$ \\
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline
+ \end{tabular}
+\caption{Test conditions: Grid 2x16 and 4x8 - Networks N1 vs N2}
+\label{tab:02}
+\end{center}
+\end{table}
+
+These experiments compare the behavior of the algorithms running first on a
+speed inter-cluster network (N1) and also on a less performant network (N2).
+Figure~\ref{fig:02} shows that end users will gain to reduce the execution time
+for both algorithms in using a grid architecture like 4x16 or 8x8: the
+performance was increased by a factor of $2$. The results depict also that when
+the network speed drops down (variation of 12.5\%), the difference between the two Multisplitting algorithms execution times can reach more than 25\%.
+%\RC{c'est pas clair : la différence entre quoi et quoi?}
+%\DL{pas clair}
+%\RCE{Modifie}
+
+
+%\begin{wrapfigure}{l}{100mm}
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf}
+\caption{Grid 2x16 and 4x8 - Networks N1 vs N2}
+\label{fig:02}
+\end{figure}
+%\end{wrapfigure}
+
+
+\subsubsection{Network latency impacts on performance}
+\ \\
+\begin{table} [ht!]
+\centering
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 2x16\\ %\hline
+ Network & N1 : bw=1Gbs \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline
+ \end{tabular}
+\caption{Test conditions: Network latency impacts}
+\label{tab:03}
+\end{table}
+
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf}
+\caption{Network latency impacts on execution time}
+\label{fig:03}
+\end{figure}
+
+
+According to the results of Figure~\ref{fig:03}, a degradation of the network
+latency from $8.10^{-6}$ to $6.10^{-5}$ implies an absolute time increase of more
+than $75\%$ (resp. $82\%$) of the execution for the classical GMRES (resp. Krylov
+multisplitting) algorithm. In addition, it appears that the Krylov
+multisplitting method tolerates more the network latency variation with a less
+rate increase of the execution time. Consequently, in the worst case
+($lat=6.10^{-5 }$), the execution time for GMRES is almost the double than the
+time of the Krylov multisplitting, even though, the performance was on the same
+order of magnitude with a latency of $8.10^{-6}$.
+
+\subsubsection{Network bandwidth impacts on performance}
+\ \\
+\begin{table} [ht!]
+\centering
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 2x16\\ %\hline
+ Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline \\
+ \end{tabular}
+\caption{Test conditions: Network bandwidth impacts}
+\label{tab:04}
+\end{table}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf}
+\caption{Network bandwith impacts on execution time}
+\label{fig:04}
+\end{figure}
+
+The results of increasing the network bandwidth show the improvement of the
+performance for both algorithms by reducing the execution time (see
+Figure~\ref{fig:04}). However, in this case, the Krylov multisplitting method
+presents a better performance in the considered bandwidth interval with a gain
+of $40\%$ which is only around $24\%$ for the classical GMRES.
+
+\subsubsection{Input matrix size impacts on performance}
+\ \\
+\begin{table} [ht!]
+\centering
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 4x8\\ %\hline
+ Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\
+ Input matrix size & N$_{x}$ = From 40 to 200\\ \hline
+ \end{tabular}
+\caption{Test conditions: Input matrix size impacts}
+\label{tab:05}
+\end{table}
+
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf}
+\caption{Problem size impacts on execution time}
+\label{fig:05}
+\end{figure}
+
+In these experiments, the input matrix size has been set from $N_{x} = N_{y}
+= N_{z} = 40$ to $200$ side elements that is from $40^{3} = 64.000$ to $200^{3}
+= 8,000,000$ points. Obviously, as shown in Figure~\ref{fig:05}, the execution
+time for both algorithms increases when the input matrix size also increases.
+But the interesting results are:
+\begin{enumerate}
+ \item the drastic increase ($10$ times) \RC{Je ne vois pas cela sur la figure}
+\RCE{Corrige} of the number of iterations needed to reach the convergence for the classical
+GMRES algorithm when the matrix size go beyond $N_{x}=150$;
+\item the classical GMRES execution time is almost the double for $N_{x}=140$
+ compared with the Krylov multisplitting method.
+\end{enumerate}
+
+These findings may help a lot end users to setup the best and the optimal
+targeted environment for the application deployment when focusing on the problem
+size scale up. It should be noticed that the same test has been done with the
+grid 2x16 leading to the same conclusion.
+
+\subsubsection{CPU Power impacts on performance}
+
+\begin{table} [ht!]
+\centering
+\begin{tabular}{r c }
+ \hline
+ Grid architecture & 2x16\\ %\hline
+ Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
+ Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline
+ \end{tabular}
+\caption{Test conditions: CPU Power impacts}
+\label{tab:06}
+\end{table}
+
+\begin{figure} [ht!]
+\centering
+\includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf}
+\caption{CPU Power impacts on execution time}
+\label{fig:06}
+\end{figure}
+
+Using the Simgrid simulator flexibility, we have tried to determine the impact
+on the algorithms performance in varying the CPU power of the clusters nodes
+from $1$ to $19$ GFlops. The outputs depicted in Figure~\ref{fig:06} confirm the
+performance gain, around $95\%$ for both of the two methods, after adding more
+powerful CPU.
+
+\DL{il faut une conclusion sur ces tests : ils confirment les résultats déjà
+obtenus en grandeur réelle. Donc c'est une aide précieuse pour les dev. Pas
+besoin de déployer sur une archi réelle}
+
+
+\subsection{Comparing GMRES in native synchronous mode and the multisplitting algorithm in asynchronous mode}
+
+The previous paragraphs put in evidence the interests to simulate the behavior
+of the application before any deployment in a real environment. In this
+section, following the same previous methodology, our goal is to compare the
+efficiency of the multisplitting method in \textit{ asynchronous mode} compared with the
+classical GMRES in \textit{synchronous mode}.
+
+The interest of using an asynchronous algorithm is that there is no more
+synchronization. With geographically distant clusters, this may be essential.
+In this case, each processor can compute its iteration freely without any
+synchronization with the other processors. Thus, the asynchronous may
+theoretically reduce the overall execution time and can improve the algorithm
+performance.
+
+\RC{la phrase suivante est bizarre, je ne comprends pas pourquoi elle vient ici}
+In this section, Simgrid simulator tool has been successfully used to show
+the efficiency of the multisplitting in asynchronous mode and to find the best
+combination of the grid resources (CPU, Network, input matrix size, \ldots ) to
+get the highest \textit{"relative gain"} (exec\_time$_{GMRES}$ /
+exec\_time$_{multisplitting}$) in comparison with the classical GMRES time.
+
+
+The test conditions are summarized in the table~\ref{tab:07}: \\
+
+\begin{table} [ht!]
+\centering
+\begin{tabular}{r c }
+ \hline
+ Grid Architecture & 2x50 totaling 100 processors\\ %\hline
+ Processors Power & 1 GFlops to 1.5 GFlops\\
+ Intra-Network & bw=1.25 Gbits - lat=5.10$^{-5}$ \\ %\hline
+ Inter-Network & bw=5 Mbits - lat=2.10$^{-2}$\\
+ Input matrix size & N$_{x}$ = From 62 to 150\\ %\hline
+ Residual error precision & 10$^{-5}$ to 10$^{-9}$\\ \hline \\
+ \end{tabular}
+\caption{Test conditions: GMRES in synchronous mode vs Krylov Multisplitting in asynchronous mode}
+\label{tab:07}
+\end{table}
+
+Again, comprehensive and extensive tests have been conducted with different
+parameters as the CPU power, the network parameters (bandwidth and latency)
+and with different problem size. The relative gains greater than $1$ between the
+two algorithms have been captured after each step of the test. In
+Figure~\ref{fig:07} are reported the best grid configurations allowing
+the multisplitting method to be more than $2.5$ times faster than the
+classical GMRES. These experiments also show the relative tolerance of the
+multisplitting algorithm when using a low speed network as usually observed with
+geographically distant clusters through the internet.
+
+% use the same column width for the following three tables
+\newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}
+\newenvironment{mytable}[1]{% #1: number of columns for data
+ \renewcommand{\arraystretch}{1.3}%
+ \begin{tabular}{|>{\bfseries}r%
+ |*{#1}{>{\centering\arraybackslash}p{\mytablew}|}}}{%
+ \end{tabular}}
+
+
+\begin{figure}[!t]
+\centering
+%\begin{table}
+% \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
+% \label{"Table 7"}
+ \begin{mytable}{11}
+ \hline
+ bandwidth (Mbit/s)
+ & 5 & 5 & 5 & 5 & 5 & 50 & 50 & 50 & 50 & 50 \\
+ \hline
+ latency (ms)
+ & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\
+ \hline
+ power (GFlops)
+ & 1 & 1 & 1 & 1.5 & 1.5 & 1.5 & 1.5 & 1 & 1.5 & 1.5 \\
+ \hline
+ size (N)
+ & 62 & 62 & 62 & 100 & 100 & 110 & 120 & 130 & 140 & 150 \\
+ \hline
+ Precision
+ & \np{E-5} & \np{E-8} & \np{E-9} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11}\\
+ \hline
+ Relative gain
+ & 2.52 & 2.55 & 2.52 & 2.57 & 2.54 & 2.53 & 2.51 & 2.58 & 2.55 & 2.54 \\
+ \hline
+ \end{mytable}
+%\end{table}
+ \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
+ \label{fig:07}
+\end{figure}
-\section{Simulation of the multisplitting method}
-
-\section{Experiments and results}
-
-\paragraph*{1. Study setup and methodology}
-
-\paragraph*{2. Factors impacting distributed applications performance in a grid environment}
-
-\paragraph*{3. Comparing GMRES and Multisplitting algorithms in synchronous mode}
-
-\paragraph*{4. Comparing GMRES in synchronous mode and Multisplitting algorithms in asynchronous mode}
\section{Conclusion}
CONCLUSION
\section*{Acknowledgment}
-
-The authors would like to thank\dots{}
+This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
-% trigger a \newpage just before the given reference
-% number - used to balance the columns on the last page
-% adjust value as needed - may need to be readjusted if
-% the document is modified later
-\bibliographystyle{IEEEtran}
-\bibliography{hpccBib}
+\bibliographystyle{wileyj}
+\bibliography{biblio}
\end{document}
