-Parallel computing and high performance computing (HPC) are becoming
-more and more imperative for solving various problems raised by
-researchers on various scientific disciplines but also by industrial in
-the field. Indeed, the increasing complexity of these requested
-applications combined with a continuous increase of their sizes lead to
-write distributed and parallel algorithms requiring significant hardware
-resources ( grid computing , clusters, broadband network ,etc... ) but
-also a non- negligible CPU execution time. We consider in this paper a
-class of highly efficient parallel algorithms called iterative executed
-in a distributed environment. As their name suggests, these algorithm
-solves a given problem that might be NP- complete complex by successive
-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
-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
-where a new iteration begin only when all nodes communications are
-completed, either "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,
-storage and high speed communication network. Indeed, limited physical
-resources are blocking factors for large-scale deployment of parallel
-algorithms.
-
-In recent years, the use of a simulation environment to execute parallel
-iterative algorithms found some interests in reducing the highly cost of
-access to computing resources: (1) for the applications development life
-cycle and in code debugging (2) and in production to get results in a
-reasonable execution time with a simulated infrastructure not accessible
-with physical resources. Indeed, the launch of distributed iterative
-asynchronous algorithms to solve a given problem on a large-scale
-simulated environment challenges to find optimal configurations giving
-the best results with a lowest residual error and in the best of
-execution time. According our knowledge, no testing of large-scale
-simulation of the class of algorithm solving to achieve real results has
-been undertaken to date. We had in the scope of this work implemented a
-program for solving large non-symmetric linear system of equations by
-numerical method GMRES (Generalized Minimal Residual ) in the simulation
-environment Simgrid . The simulated platform had allowed us to launch
-the application from a modest computing infrastructure by simulating
-different distributed architectures composed by clusters nodes
-interconnected by variable speed networks. In addition, it has been
-permitted to show the effectiveness of asynchronous mode algorithm by
-comparing its performance with the synchronous mode time. With selected
-parameters on the network platforms (bandwidth, latency of inter cluster
-network) and on the clusters architecture (number, capacity calculation
-power) in the simulated environment , the experimental results have
-demonstrated not only the algorithm convergence within a reasonable time
-compared with the physical environment performance, but also a time
-saving of up to 40 \% in asynchronous mode.
-
-This article is structured as follows: after this introduction, the next
-section will give a brief description of iterative asynchronous model.
-Then, the simulation framework SIMGRID will be presented with the
-settings to create various distributed architectures. The algorithm of
-the multi -splitting method used by GMRES written with MPI primitives
-and its adaptation to Simgrid with SMPI (Simulation MPI ) will be in the
-next section . At last, the experiments results carried out will be
-presented before the conclusion which we will announce the opening of
-our future work after the results.
-
-\section{The asynchronous iteration model}
+Parallel computing and high performance computing (HPC) are becoming more and more imperative for solving various
+problems raised by researchers on various scientific disciplines but also by industrial in the field. Indeed, the
+increasing complexity of these requested applications combined with a continuous increase of their sizes lead to write
+distributed and parallel algorithms requiring significant hardware resources (grid computing, clusters, broadband
+network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient
+parallel algorithms called \emph{iterative algorithms} executed in a distributed environment. As their name
+suggests, these algorithms solve a given problem by successive 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~\cite{BT89,Bahi07}.
+
+Parallelization of such algorithms generally involves the division of the problem
+into several \emph{blocks} that will be solved in parallel on multiple
+processing units. The latter will communicate each intermediate results before a
+new iteration starts and until the approximate solution is reached. These
+parallel computations can be performed either in \emph{synchronous} mode where a
+new iteration begins only when all nodes communications are completed, or in
+\emph{asynchronous} mode where processors can continue independently with no
+synchronization points~\cite{bcvc06:ij}. In this case, local computations do not
+need to wait for required data. Processors can then perform their iterations
+with the data present at that time. Even if the number of iterations required
+before the convergence is generally greater than for the synchronous case,
+asynchronous iterative algorithms can significantly reduce overall execution
+times by suppressing idle times due to synchronizations especially in a grid
+computing context (see~\cite{Bahi07} for more details).
+
+Parallel applications based on a (synchronous or asynchronous) iteration model
+may have different configuration and deployment requirements. Quantifying their
+resource allocation policies and application scheduling algorithms in grid
+computing environments under varying load, CPU power and network speeds is very
+costly, very labor intensive and very time
+consuming~\cite{Calheiros:2011:CTM:1951445.1951450}. The case of asynchronous
+iterative algorithms is even more problematic since they are very sensible to
+the execution environment context. For instance, variations in the network
+bandwidth (intra and inter-clusters), in the number and the power of nodes, in
+the number of clusters\dots{} can lead to very different number of iterations
+and so to very different execution times. Then, it appears that the use of
+simulation tools to explore various platform scenarios and to run large numbers
+of experiments quickly can be very promising. In this way, the use of a
+simulation environment to execute parallel iterative algorithms found some
+interests in reducing the highly cost of access to computing resources: (1) for
+the applications development life cycle and in code debugging (2) and in
+production to get results in a reasonable execution time with a simulated
+infrastructure not accessible with physical resources. Indeed, the launch of
+distributed iterative asynchronous algorithms to solve a given problem on a
+large-scale simulated environment challenges to find optimal configurations
+giving the best results with a lowest residual error and in the best of
+execution time.
+
+
+To our knowledge, there is no existing work on the large-scale simulation of a
+real asynchronous iterative application. {\bf The contribution of the present
+ paper can be summarized in two main points}. First we give a first approach
+of the simulation of asynchronous iterative algorithms using a simulation tool
+(i.e. the SimGrid toolkit~\cite{SimGrid}). Second, we confirm the
+effectiveness of the asynchronous multisplitting algorithm by comparing its
+performance with the synchronous GMRES (Generalized Minimal Residual) method
+\cite{ref1}. Both these codes can be used to solve large linear systems. In
+this paper, we focus on a 3D Poisson problem. We show, that with minor
+modifications of the initial MPI code, the SimGrid toolkit allows us to perform
+a test campaign of a real asynchronous iterative application on different
+computing architectures.
+% The simulated results we
+%obtained are in line with real results exposed in ??\AG[]{ref?}.
+SimGrid had allowed us to launch the application from a modest computing
+infrastructure by simulating different distributed architectures composed by
+clusters nodes interconnected by variable speed networks. Parameters of the
+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. 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
+
+
+
+This article is structured as follows: after this introduction, the next section
+will give a brief description of iterative asynchronous model. Then, the
+simulation framework SimGrid is presented with the settings to create various
+distributed architectures. Then, the multisplitting method is presented, it is
+based on GMRES to solve each block obtained of the splitting. This code is
+written with MPI primitives and its adaptation to SimGrid with SMPI (Simulated
+MPI) is detailed in the next section. At last, the simulation results carried
+out will be presented before some concluding remarks and future works.
