X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/c9f1e655cef3e735867e6000202cb1f982f05d58..2fcbf808f481976ed501aaec9b1446407e7a4f72:/hpcc.tex?ds=inline diff --git a/hpcc.tex b/hpcc.tex index 5fbeca1..4ff46b6 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -1,577 +1,762 @@ - -%% bare_conf.tex -%% V1.3 -%% 2007/01/11 -%% by Michael Shell -%% See: -%% http://www.michaelshell.org/ -%% for current contact information. -%% -%% This is a skeleton file demonstrating the use of IEEEtran.cls -%% (requires IEEEtran.cls version 1.7 or later) with an IEEE conference paper. -%% -%% Support sites: -%% http://www.michaelshell.org/tex/ieeetran/ -%% http://www.ctan.org/tex-archive/macros/latex/contrib/IEEEtran/ -%% and -%% http://www.ieee.org/ - -%%************************************************************************* -%% Legal Notice: -%% This code is offered as-is without any warranty either expressed or -%% implied; without even the implied warranty of MERCHANTABILITY or -%% FITNESS FOR A PARTICULAR PURPOSE! -%% User assumes all risk. -%% In no event shall IEEE or any contributor to this code be liable for -%% any damages or losses, including, but not limited to, incidental, -%% consequential, or any other damages, resulting from the use or misuse -%% of any information contained here. -%% -%% All comments are the opinions of their respective authors and are not -%% necessarily endorsed by the IEEE. -%% -%% This work is distributed under the LaTeX Project Public License (LPPL) -%% ( http://www.latex-project.org/ ) version 1.3, and may be freely used, -%% distributed and modified. 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However, -% if the conference you are submitting to favors bold math in the abstract, -% then you can use LaTeX's standard command \boldmath at the very start -% of the abstract to achieve this. Many IEEE journals/conferences frown on -% math in the abstract anyway. - -% no keywords - - - - -% For peer review papers, you can put extra information on the cover -% page as needed: -% \ifCLASSOPTIONpeerreview -% \begin{center} \bfseries EDICS Category: 3-BBND \end{center} -% \fi -% -% For peerreview papers, this IEEEtran command inserts a page break and -% creates the second title. It will be ignored for other modes. -\IEEEpeerreviewmaketitle - +Synchronous iterative algorithms are often less scalable than asynchronous +iterative ones. Performing large scale experiments with different kind of +network parameters is not easy because with supercomputers such parameters are +fixed. So, one solution consists in using simulations first in order to analyze +what parameters could influence or not the behavior of an algorithm. In this +paper, we show that it is interesting to use SimGrid to simulate the behavior +of asynchronous iterative algorithms. For that, we compare the behavior of a +synchronous GMRES algorithm with an asynchronous multisplitting one with +simulations which let us easily choose some parameters. Both codes are real MPI +codes and simulations allow us to see when the asynchronous multisplitting algorithm can be more +efficient than the GMRES one to solve a 3D Poisson problem. + + +% no keywords for IEEE conferences +% Keywords: Algorithm distributed iterative asynchronous simulation SimGrid +\end{abstract} \section{Introduction} -Présenter un bref état de l'art sur la simulation d'algos parallèles. Présenter rapidement les algos itératifs asynchrones et leurs avantages. Parler de leurs inconvénients en particulier la difficulté de déploiement à grande échelle donc il serait bien de simuler. Dire qu'à notre connaissance il n'existe pas de simulation de ce type d'algo. -Présenter les travaux et les résultats obtenus. Annoncer le plan. - -\section{The asynchronous iteration model} - -Décrire le modèle asynchrone. Je m'en charge (DL) +Parallel computing and high performance computing (HPC) are becoming more and more imperative to solve various +problems raised by researchers on various scientific disciplines but also by industrialists 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 a \emph{synchronous} mode, where a +new iteration begins only when all nodes communications are completed, or in an +\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 required iterations +before the convergence is generally greater than in 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 are 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 sensitive 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. + +Thus, using a simulation environment to execute parallel iterative algorithms can prove to be very interesting to reduce 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, to find optimal configurations +giving the best results with a lowest residual error and in the best +execution time is very challenging for large scale distributed iterative asynchronous algorithms + + +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 +efficiency of the asynchronous multisplitting algorithm by comparing its +performances 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 has 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 the 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 from 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. -\section{SimGrid} + +\section{Motivations and scientific context} + +As described in the introduction, parallel iterative methods are now widely used +in many scientific domains. They can be classified in three main classes +depending on how iterations and communications are managed (for more details +readers can refer to~\cite{bcvc06:ij}). In the synchronous iterations model, +data are exchanged at the end of each iteration. All the processors must begin +the same iteration at the same time and important idle times on processors are +generated. It is possible to use asynchronous communications, in this case, the +model can be compared to the previous one except that data required on another +processor are sent asynchronously i.e. without stopping current computations. +This technique allows communications to be partially overlapped by computations +but unfortunately, the overlapping is only partial and important idle times +remain. It is clear that, in a grid computing context, where the number of +computational nodes is large, heterogeneous and widely distributed, the idle +times generated by synchronizations are very penalizing. One way to overcome +this problem is to use the asynchronous iterations model. Here, local +computations do not need to wait for required data. Processors can then perform +their iterations with the data present at that time. Figure~\ref{fig:aiac} +illustrates this model where the gray blocks represent the computation phases. +With this algorithmic model, the number of iterations required before the +convergence is generally greater than for the two former classes. But, and as +detailed in~\cite{bcvc06:ij}, asynchronous iterative algorithms can +significantly reduce overall execution times by suppressing idle times due to +synchronizations especially in a grid computing context. + +\begin{figure}[!t] + \centering + \includegraphics[width=8cm]{AIAC.pdf} + \caption{The asynchronous iterations model} + \label{fig:aiac} +\end{figure} + + +%% It is very challenging to develop efficient applications for large scale, +%% heterogeneous and distributed platforms such as computing grids. Researchers and +%% engineers have to develop techniques for maximizing application performance of +%% these multi-cluster platforms, by redesigning the applications and/or by using +%% novel algorithms that can account for the composite and heterogeneous nature of +%% the platform. Unfortunately, the deployment of such applications on these very +%% large scale systems is very costly, labor intensive and time consuming. In this +%% context, it appears that the use of simulation tools to explore various platform +%% scenarios at will and to run enormous numbers of experiments quickly can be very +%% promising. Several works\dots{} + +%% \AG{Several works\dots{} what?\\ +% Le paragraphe suivant se trouve déjà dans l'intro ?} +In the context of asynchronous algorithms, the number of iterations to reach the +convergence depends on the delay of the messages. With synchronous iterations, the +number of iterations is exactly the same than in the sequential mode (if the +parallelization process does not change the algorithm). So the difficulty with +asynchronous iterative algorithms comes from the fact that it is necessary to run the algorithm +with real data. Indeed, from one execution to the other the order of messages will +change and the number of iterations to reach the convergence will also change. +According to all the parameters of the platform (number of nodes, power of +nodes, inter and intra clusters bandwidth and latency, etc.) and of the +algorithm (number of splittings with the multisplitting algorithm), the +multisplitting code will obtain the solution more or less quickly. Of course, +the GMRES method also depends on the same parameters. As it is difficult to have +access to many clusters, grids or supercomputers with many different network +parameters, it is interesting to be able to simulate the behavior of +asynchronous iterative algorithms before being able to run real experiments. -Décrire SimGrid (Arnaud) -\section{Simulation of the multi-splitting method} -Décrire le problème (algo) traité ainsi que le processus d'adaptation à SimGrid. -\section{Experimental results} -\section{Conclusion} +\section{SimGrid} -% An example of a floating figure using the graphicx package. -% Note that \label must occur AFTER (or within) \caption. -% For figures, \caption should occur after the \includegraphics. -% Note that IEEEtran v1.7 and later has special internal code that -% is designed to preserve the operation of \label within \caption -% even when the captionsoff option is in effect. However, because -% of issues like this, it may be the safest practice to put all your -% \label just after \caption rather than within \caption{}. -% -% Reminder: the "draftcls" or "draftclsnofoot", not "draft", class -% option should be used if it is desired that the figures are to be -% displayed while in draft mode. -% -%\begin{figure}[!t] -%\centering -%\includegraphics[width=2.5in]{myfigure} -% where an .eps filename suffix will be assumed under latex, -% and a .pdf suffix will be assumed for pdflatex; or what has been declared -% via \DeclareGraphicsExtensions. -%\caption{Simulation Results} -%\label{fig_sim} -%\end{figure} - -% Note that IEEE typically puts floats only at the top, even when this -% results in a large percentage of a column being occupied by floats. - - -% An example of a double column floating figure using two subfigures. -% (The subfig.sty package must be loaded for this to work.) -% The subfigure \label commands are set within each subfloat command, the -% \label for the overall figure must come after \caption. -% \hfil must be used as a separator to get equal spacing. -% The subfigure.sty package works much the same way, except \subfigure is -% used instead of \subfloat. -% -%\begin{figure*}[!t] -%\centerline{\subfloat[Case I]\includegraphics[width=2.5in]{subfigcase1}% -%\label{fig_first_case}} -%\hfil -%\subfloat[Case II]{\includegraphics[width=2.5in]{subfigcase2}% -%\label{fig_second_case}}} -%\caption{Simulation results} -%\label{fig_sim} -%\end{figure*} +SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile} +is a simulation framework to study the behavior of large-scale distributed +systems. As its name suggests, it emanates from the grid computing community, +but is nowadays used to study grids, clouds, HPC or peer-to-peer systems. The +early versions of SimGrid date back from 1999, but it is still actively +developed and distributed as an open source software. Today, it is one of the +major generic tools in the field of simulation for large-scale distributed +systems. + +SimGrid provides several programming interfaces: MSG to simulate Concurrent +Sequential Processes, SimDAG to simulate DAGs of (parallel) tasks, and SMPI to +run real applications written in MPI~\cite{MPI}. Apart from the native C +interface, SimGrid provides bindings for the C++, Java, Lua and Ruby programming +languages. SMPI is the interface that has been used for the work described in +this paper. The SMPI interface implements about \np[\%]{80} of the MPI 2.0 +standard~\cite{bedaride+degomme+genaud+al.2013.toward}, and supports +applications written in C or Fortran, with little or no modifications. + +Within SimGrid, the execution of a distributed application is simulated by a +single process. The application code is really executed, but some operations, +like communications, are intercepted, and their running time is computed +according to the characteristics of the simulated execution platform. The +description of this target platform is given as an input for the execution, by +means of an XML file. It describes the properties of the platform, such as +the computing nodes with their computing power, the interconnection links with +their bandwidth and latency, and the routing strategy. The scheduling of the +simulated processes, as well as the simulated running time of the application +are computed according to these properties. + +To compute the durations of the operations in the simulated world, and to take +into account resource sharing (e.g. bandwidth sharing between competing +communications), SimGrid uses a fluid model. This allows users to run relatively fast +simulations, while still keeping accurate +results~\cite{bedaride+degomme+genaud+al.2013.toward, + velho+schnorr+casanova+al.2013.validity}. Moreover, depending on the +simulated application, SimGrid/SMPI allows to skip long lasting computations and +to only take their duration into account. When the real computations cannot be +skipped, but the results are unimportant for the simulation results, it is +also possible to share dynamically allocated data structures between +several simulated processes, and thus to reduce the whole memory consumption. +These two techniques can help to run simulations on a very large scale. + +The validity of simulations with SimGrid has been asserted by several studies. +See, for example, \cite{velho+schnorr+casanova+al.2013.validity} and articles +referenced therein for the validity of the network models. Comparisons between +real execution of MPI applications on the one hand, and their simulation with +SMPI on the other hand, are presented in~\cite{guermouche+renard.2010.first, + clauss+stillwell+genaud+al.2011.single, + bedaride+degomme+genaud+al.2013.toward}. All these works conclude that +SimGrid is able to simulate pretty accurately the real behavior of the +applications. + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Simulation of the multisplitting method} + +\subsection{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 $b$ 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~\cite{o1985multi}. In this case, we apply a row-by-row splitting without overlapping +\begin{equation*} + \left(\begin{array}{ccc} + A_{11} & \cdots & A_{1L} \\ + \vdots & \ddots & \vdots\\ + A_{L1} & \cdots & A_{LL} + \end{array} \right) + \times + \left(\begin{array}{c} + X_1 \\ + \vdots\\ + X_L + \end{array} \right) + = + \left(\begin{array}{c} + B_1 \\ + \vdots\\ + B_L + \end{array} \right) +\end{equation*} +in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ +are assigned to one cluster, where for all $\ell,m\in\{1,\ldots,L\}$, $A_{\ell + m}$ is a rectangular block of $A$ of size $n_\ell\times n_m$, $X_\ell$ and +$B_\ell$ are sub-vectors of $x$ and $b$, respectively, of size $n_\ell$ each, +and $\sum_{\ell} n_\ell=\sum_{m} n_m=n$. + +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} + \label{eq:4.1} + \left\{ + \begin{array}{l} + A_{\ell\ell}X_\ell = Y_\ell \text{, such that}\\ + Y_\ell = B_\ell - \displaystyle\sum_{\substack{m=1\\ m\neq \ell}}^{L}A_{\ell m}X_m + \end{array} + \right. +\end{equation} +is solved independently by a cluster and communications are required to update +the right-hand side sub-vector $Y_\ell$, 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{figure}[!t] + %%% IEEE instructions forbid to use an algorithm environment here, use figure + %%% instead +\begin{algorithmic}[1] +\Input $A_\ell$ (sparse sub-matrix), $B_\ell$ (right-hand side sub-vector) +\Output $X_\ell$ (solution sub-vector)\medskip + +\State Load $A_\ell$, $B_\ell$ +\State Set the initial guess $x^0$ +\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\label{algo:01:send} Send shared elements of $X_\ell^{k+1}$ to neighboring clusters +\State\label{algo:01:recv} Receive shared elements in $\{X_m^{k+1}\}_{m\neq \ell}$ +\EndFor + +\Statex + +\Function {InnerSolver}{$x^0$, $k$} +\State Compute local right-hand side $Y_\ell$: + \begin{equation*} + Y_\ell = B_\ell - \sum\nolimits^L_{\substack{m=1\\ m\neq \ell}}A_{\ell m}X_m^0 + \end{equation*} +\State Solving sub-system $A_{\ell\ell}X_\ell^k=Y_\ell$ with the parallel GMRES method +\State \Return $X_\ell^k$ +\EndFunction +\end{algorithmic} +\caption{A multisplitting solver with GMRES method} +\label{algo:01} +\end{figure} + +The algorithm in Figure~\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 $\ell,m\in\{1,\ldots,L\}$, the matrices and +vectors with the subscript $\ell$ represent the local data for cluster $\ell$, +while $\{A_{\ell m}\}_{m\neq \ell}$ are off-diagonal matrices of sparse matrix +$A$ and $\{X_m\}_{m\neq \ell}$ 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~\ref{algo:01:send} and~\ref{algo:01:recv} in +Figure~\ref{algo:01}). The shared vector elements of the solution $x$ are +exchanged by message passing using MPI non-blocking communication routines. + +\begin{figure}[!t] +\centering + \includegraphics[width=60mm,keepaspectratio]{clustering} +\caption{Example of three distant clusters of processors.} +\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 $\ell$ +sets the token to \textit{True} if the local convergence is achieved or to +\textit{False} otherwise, and sends it to master processor $\ell+1$. Finally, the +global convergence is detected when the master of cluster 1 receives from the +master of cluster $L$ a token set to \textit{True}. In this case, the master of +cluster 1 broadcasts a stop message to the masters of other clusters. In this work, +the local convergence on each cluster $\ell$ is detected when the following +condition is satisfied +\begin{equation*} + (k\leq \MI) \text{ or } (\|X_\ell^k - X_\ell^{k+1}\|_{\infty}\leq\epsilon) +\end{equation*} +where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the +tolerance threshold of the error computed between two successive local solution +$X_\ell^k$ and $X_\ell^{k+1}$. + + + +In this paper, we solve the 3D Poisson problem whose mathematical model is +\begin{equation} +\left\{ +\begin{array}{l} +\nabla^2 u = f \text{~in~} \Omega \\ +u =0 \text{~on~} \Gamma =\partial\Omega +\end{array} +\right. +\label{eq:02} +\end{equation} +where $\nabla^2$ is the Laplace operator, $f$ and $u$ are real-valued functions, and $\Omega=[0,1]^3$. The spatial discretization with a finite differences scheme reduces problem~(\ref{eq:02}) to a system of sparse linear equations. Our multisplitting method solves the 3D Poisson problem using a seven point stencil whose general expression could be written as +\begin{equation} +\begin{array}{l} +u(x-1,y,z) + u(x,y-1,z) + u(x,y,z-1)\\+u(x+1,y,z)+u(x,y+1,z)+u(x,y,z+1) \\ -6u(x,y,z)=h^2f(x,y,z), +%u(x,y,z)= & \frac{1}{6}\times [u(x-1,y,z) + u(x+1,y,z) + \\ + % & u(x,y-1,z) + u(x,y+1,z) + \\ + % & u(x,y,z-1) + u(x,y,z+1) - \\ & h^2f(x,y,z)], +\end{array} +\label{eq:03} +\end{equation} +where $h$ is the distance between two adjacent elements in the spatial discretization scheme and the iteration matrix $A$ of size $N_x\times N_y\times N_z$ of the discretized linear system is sparse, symmetric and positive definite. + +The parallel solving of the 3D Poisson problem with our multisplitting method requires a data partitioning of the problem between clusters and between processors within a cluster. We have chosen the 3D partitioning instead of the row-by-row partitioning one in order to reduce the data exchanges at sub-domain boundaries. Figure~\ref{fig:4.2} shows an example of the data partitioning of the 3D Poisson problem between two clusters of processors, where each sub-problem is assigned to a processor. In this context, a processor has at most six neighbors within a cluster or in distant clusters with which it shares data at sub-domain boundaries. + +\begin{figure}[!t] +\centering + \includegraphics[width=80mm,keepaspectratio]{partition} +\caption{Example of the 3D data partitioning between two clusters of processors.} +\label{fig:4.2} +\end{figure} + + +\subsection{Simulation of the multisplitting method using SimGrid and SMPI} + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +We did not encounter major blocking problems when adapting the multisplitting algorithm previously described to a simulation environment like SimGrid unless some code +debugging. Indeed, apart from the review of the program sequence for asynchronous exchanges between processors within a cluster or between clusters, the algorithm was executed successfully with SMPI and provided identical outputs as those obtained with direct execution under MPI. For the synchronous GMRES method, the execution of the program raised no particular issue but in the asynchronous multisplitting method, the review of the sequence of \texttt{MPI\_Isend, MPI\_Irecv} and \texttt{MPI\_Waitall} instructions +and with the addition of the primitive \texttt{MPI\_Test} was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm. +%\CER{On voulait en fait montrer la simplicité de l'adaptation de l'algo a SimGrid. Les problèmes rencontrés décrits dans ce paragraphe concerne surtout le mode async}\LZK{OK. J'aurais préféré avoir un peu plus de détails sur l'adaptation de la version async} +%\CER{Le problème majeur sur l'adaptation MPI vers SMPI pour la partie asynchrone de l'algorithme a été le plantage en SMPI de Waitall après un Isend et Irecv. J'avais proposé un workaround en utilisant un MPI\_wait séparé pour chaque échange a la place d'un waitall unique pour TOUTES les échanges, une instruction qui semble bien fonctionner en MPI. Ce workaround aussi fonctionne bien. Mais après, tu as modifié le programme avec l'ajout d'un MPI\_Test, au niveau de la routine de détection de la convergence et du coup, l'échange global avec waitall a aussi fonctionné.} +Note here that the use of SMPI functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation. +As mentioned, upon this adaptation, the algorithm is executed as in real life in the simulated environment after the following minor changes. The scope of all declared +global variables have been moved to local subroutines. Indeed, global variables generate side effects arising from the concurrent access of +shared memory used by threads simulating each computing unit in the SimGrid architecture. +%Second, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid. +%\AG{compilation or run-time error?} +In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real +environment. We have successfully executed the code for the synchronous GMRES algorithm compared with our asynchronous multisplitting algorithm after few modifications. + + + +\section{Simulation results} + +When the \textit{real} application runs in the simulation environment and produces the expected results, varying the input +parameters and the program arguments allows us to compare outputs from the code execution. We have noticed from this +study that the results depend on the following parameters: +\begin{itemize} +\item At the network level, we found that the most critical values are the + bandwidth and the network latency. +\item Host processor power (GFlops) can also influence the results. +\item Finally, when submitting job batches for execution, the arguments values + passed to the program like the maximum number of iterations or the precision are critical. They allow us to ensure not only the convergence of the + algorithm but also to get the main objective in getting an execution time with the asynchronous multisplitting less than with synchronous GMRES. + \end{itemize} + +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 +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 +synchronous mode allowing to get a relative gain greater than 1. This action +simulates the case of distant clusters linked with long distance networks as in grid computing context. + + + +Both codes were simulated on a two clusters based network with 50 hosts each, totalling 100 hosts. Various combinations of the above +factors have provided the results shown in Table~\ref{tab.cluster.2x50}. The problem size of the 3D Poisson problem ranges from $N=N_x = N_y = N_z = \text{62}$ to 150 elements (that is from +$\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = +\text{\np{3375000}}$ entries). With the asynchronous multisplitting algorithm the simulated execution time is on average 2.5 times faster than with the synchronous GMRES one. +%\AG{Expliquer comment lire les tableaux.} +%\CER{J'ai reformulé la phrase par la lecture du tableau. Plus de détails seront lus dans la partie Interprétations et commentaires} +% 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{table}[!t] + \centering + \caption{Relative gain of the multisplitting algorithm compared to GMRES for + different configurations with 2 clusters, each one composed of 50 nodes. Latency = $20$ms} + \label{tab.cluster.2x50} + + \begin{mytable}{5} + \hline + bandwidth (Mbit/s) + & 5 & 5 & 5 & 5 & 5 \\ + \hline + % latency (ms) + % & 20 & 20 & 20 & 20 & 20 \\ + %\hline + power (GFlops) + & 1 & 1 & 1 & 1.5 & 1.5 \\ + \hline + size $(N)$ + & $62^3$ & $62^3$ & $62^3$ & $100^3$ & $100^3$ \\ + \hline + Precision + & \np{E-5} & \np{E-8} & \np{E-9} & \np{E-11} & \np{E-11} \\ + \hline + \hline + Relative gain + & 2.52 & 2.55 & 2.52 & 2.57 & 2.54 \\ + \hline + \end{mytable} + + \bigskip + + \begin{mytable}{5} + \hline + bandwidth (Mbit/s) + & 50 & 50 & 50 & 50 & 50 \\ % & 10 & 10 \\ + \hline + %latency (ms) + %& 20 & 20 & 20 & 20 & 20 \\ % & 0.03 & 0.01 \\ + %\hline + Power (GFlops) + & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 \\ % & 1 & 1.5 \\ + \hline + size $(N)$ + & $110^3$ & $120^3$ & $130^3$ & $140^3$ & $150^3$ \\ % & 171 & 171 \\ + \hline + Precision + & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5} & \np{E-5} \\ + \hline + \hline + Relative gain + & 2.53 & 2.51 & 2.58 & 2.55 & 2.54 \\ % & 1.59 & 1.29 \\ + \hline + \end{mytable} +\end{table} + +%\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la table?} + +%Then we have changed the network configuration using three clusters containing +%respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the +%clusters. In the same way as above, a judicious choice of key parameters has +%permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the +%relative gains greater than 1 with a matrix size from 62 to 100 elements. + +%\CER{En accord avec RC, on a pour le moment enlevé les tableaux 2 et 3 sachant que les résultats obtenus sont limites. De même, on a enlevé aussi les deux dernières colonnes du tableau I en attendant une meilleure performance et une meilleure precision} +%\begin{table}[!t] +% \centering +% \caption{3 clusters, each with 33 nodes} +% \label{tab.cluster.3x33} % -% Note that often IEEE papers with subfigures do not employ subfigure -% captions (using the optional argument to \subfloat), but instead will -% reference/describe all of them (a), (b), etc., within the main caption. +% \begin{mytable}{6} +% \hline +% bandwidth +% & 10 & 5 & 4 & 3 & 2 & 6 \\ +% \hline +% latency +% & 0.01 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\ +% \hline +% power +% & 1 & 1 & 1 & 1 & 1 & 1 \\ +% \hline +% size +% & 62 & 100 & 100 & 100 & 100 & 171 \\ +% \hline +% Prec/Eprec +% & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} \\ +% \hline +% \hline +% Relative gain +% & 1.003 & 1.01 & 1.08 & 1.19 & 1.28 & 1.01 \\ +% \hline +% \end{mytable} +%\end{table} +%In a final step, results of an execution attempt to scale up the three clustered +%configuration but increasing by two hundreds hosts has been recorded in +%Table~\ref{tab.cluster.3x67}. -% An example of a floating table. Note that, for IEEE style tables, the -% \caption command should come BEFORE the table. Table text will default to -% \footnotesize as IEEE normally uses this smaller font for tables. -% The \label must come after \caption as always. -% %\begin{table}[!t] -%% increase table row spacing, adjust to taste -%\renewcommand{\arraystretch}{1.3} -% if using array.sty, it might be a good idea to tweak the value of -% \extrarowheight as needed to properly center the text within the cells -%\caption{An Example of a Table} -%\label{table_example} -%\centering -%% Some packages, such as MDW tools, offer better commands for making tables -%% than the plain LaTeX2e tabular which is used here. -%\begin{tabular}{|c||c|} -%\hline -%One & Two\\ -%\hline -%Three & Four\\ -%\hline -%\end{tabular} +% \centering +% \caption{3 clusters, each with 66 nodes} +% \label{tab.cluster.3x67} +% +% \begin{mytable}{1} +% \hline +% bandwidth & 1 \\ +% \hline +% latency & 0.02 \\ +% \hline +% power & 1 \\ +% \hline +% size & 62 \\ +% \hline +% Prec/Eprec & \np{E-5} \\ +% \hline +% \hline +% Relative gain & 1.11 \\ +% \hline +% \end{mytable} %\end{table} +Note that the program was run with the following parameters: + +\paragraph*{SMPI parameters} + +\begin{itemize} +\item HOSTFILE: Text file containing the list of the processors units name. Here 100 hosts; +\item PLATFORM: XML file description of the platform architecture with the + following characteristics: + % two clusters (cluster1 and cluster2) with the following characteristics: + \begin{itemize} + \item 2 clusters of 50 hosts each; + \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5}; + \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{50}; + \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[ms]{20}; + \end{itemize} +\end{itemize} + + +\paragraph*{Arguments of the program} + +\begin{itemize} +\item Description of the cluster architecture matching the format ; +\item Maximum numbers of outer and inner iterations; +\item Outer and inner precisions on the residual error; +\item Matrix size $N_x$, $N_y$ and $N_z$; +\item Matrix diagonal value: $6$ (see Equation~(\ref{eq:03})); +\item Matrix off-diagonal values: $-1$; +\item Communication mode: asynchronous. +\end{itemize} + +\paragraph*{Interpretations and comments} + +After analyzing the outputs, generally, for the two clusters including one hundred hosts configuration (Tables~\ref{tab.cluster.2x50}), some combinations of parameters affecting +the results, have given a relative gain of more than 2.5, showing the effectiveness of the +asynchronous multisplitting compared to GMRES with two distant clusters. + +With these settings, Table~\ref{tab.cluster.2x50} shows +that after setting the bandwidth of the inter cluster network to \np[Mbit/s]{5}, the latency to $20$ millisecond and the processor power +to one GFlops, an efficiency of about \np[\%]{40} is +obtained in asynchronous mode for a matrix size of $62^3$ elements. It is noticed that the result remains +stable even if the residual error precision varies from \np{E-5} to \np{E-9}. By +increasing the matrix size up to $100^3$ elements, it was necessary to increase the +CPU power by \np[\%]{50} to \np[GFlops]{1.5} to get the algorithm convergence and the same order of asynchronous mode efficiency. Maintaining a relative gain of $2.5$ and such processor power but increasing network throughput inter cluster up to \np[Mbit/s]{50}, is obtained with +high external precision of \np{E-11} for a matrix size from $110^3$ to $150^3$ side +elements. + +%For the 3 clusters architecture including a total of 100 hosts, +%Table~\ref{tab.cluster.3x33} shows that it was difficult to have a combination +%which gives a relative gain of asynchronous mode more than 1.2. Indeed, for a +%matrix size of 62 elements, equality between the performance of the two modes +%(synchronous and asynchronous) is achieved with an inter cluster of +%\np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency greater than 1.2 with a matrix %size of 100 points, it was necessary to degrade the +%inter cluster network bandwidth from 5 to \np[Mbit/s]{2}. +%\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ??? + %Quelle est la perte de perfs en faisant ça ?} + +%A last attempt was made for a configuration of three clusters but more powerful +%with 200 nodes in total. The convergence with a relative gain around 1.1 was +%obtained with a bandwidth of \np[Mbit/s]{1} as shown in +%Table~\ref{tab.cluster.3x67}. + +%\RC{Est ce qu'on sait expliquer pourquoi il y a une telle différence entre les résultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas très bons... Je me demande s'il ne faut pas les enlever...} +%\RC{En fait je pense avoir la réponse à ma remarque... On voit avec les 2 clusters que le gain est d'autant plus grand qu'on choisit une bonne précision. Donc, plusieurs solutions, lancer rapidement un long test pour confirmer ca, ou enlever des tests... ou on ne change rien :-)} +%\LZK{Ma question est: le bandwidth et latency sont ceux inter-clusters ou pour les deux inter et intra cluster??} +%\CER{Définitivement, les paramètres réseaux variables ici se rapportent au réseau INTER cluster.} +\section{Conclusion} +The simulation of the execution of parallel asynchronous iterative algorithms on large scale clusters has been presented. +In this work, we show that SimGrid is an efficient simulation tool that has enabled us to +reach the following two objectives: + +\begin{enumerate} +\item To have a flexible configurable execution platform that allows us to + simulate algorithms for which execution of all parts of + the code is necessary. Using simulations before real executions is a nice + solution to detect potential scalability problems. + +\item To test the combination of the cluster and network specifications permitting to execute an asynchronous algorithm faster than a synchronous one. +\end{enumerate} +Our results have shown that with two distant clusters, the asynchronous multisplitting method is faster by \np[\%]{40} compared to the synchronous GMRES method +which is not negligible for solving complex practical problems with ever increasing size. + +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. + +In future 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 better experimentally validate our study. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods. -% Note that IEEE does not put floats in the very first column - or typically -% anywhere on the first page for that matter. Also, in-text middle ("here") -% positioning is not used. Most IEEE journals/conferences use top floats -% exclusively. Note that, LaTeX2e, unlike IEEE journals/conferences, places -% footnotes above bottom floats. This can be corrected via the \fnbelowfloat -% command of the stfloats package. - - - - - - - -% conference papers do not normally have an appendix - - -% use section* for acknowledgement \section*{Acknowledgment} - -The authors would like to thank... - - - - +This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01). +%\todo[inline]{The authors would like to thank\dots{}} % 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 -%\IEEEtriggeratref{8} -% The "triggered" command can be changed if desired: -%\IEEEtriggercmd{\enlargethispage{-5in}} - -% references section - -% can use a bibliography generated by BibTeX as a .bbl file -% BibTeX documentation can be easily obtained at: -% http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/ -% The IEEEtran BibTeX style support page is at: -% http://www.michaelshell.org/tex/ieeetran/bibtex/ \bibliographystyle{IEEEtran} -% argument is your BibTeX string definitions and bibliography database(s) -\bibliography{bib/hpccBib} -% -% manually copy in the resultant .bbl file -% set second argument of \begin to the number of references -% (used to reserve space for the reference number labels box) -%\begin{thebibliography}{1} -% -%\bibitem{IEEEhowto:kopka} -%H.~Kopka and P.~W. Daly, \emph{A Guide to \LaTeX}, 3rd~ed.\hskip 1em plus -% 0.5em minus 0.4em\relax Harlow, England: Addison-Wesley, 1999. -% -%\end{thebibliography} - +\bibliography{IEEEabrv,hpccBib} -% that's all folks \end{document} - +%%% Local Variables: +%%% mode: latex +%%% TeX-master: t +%%% fill-column: 80 +%%% ispell-local-dictionary: "american" +%%% End: + +% LocalWords: Ramamonjisoa Laiymani Arnaud Giersch Ziane Khodja Raphaël Femto +% LocalWords: Université Franche Comté IUT Montbéliard Maréchal Juin Inria Sud +% LocalWords: Ouest Vieille Talence cedex scalability experimentations HPC MPI +% LocalWords: Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua +% LocalWords: Fortran GFlops priori Mbit de du fcomte multisplitting scalable +% LocalWords: SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib Gbit +% LocalWords: intra durations nonsingular Waitall discretization discretized +% LocalWords: InnerSolver Isend Irecv parallelization