X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/a091bb5ea8523eab4cda5bd035da309c347c95e5..9e58442a55b1f2eda6edb277b367dc4e34f1fa1e:/hpcc.tex?ds=inline diff --git a/hpcc.tex b/hpcc.tex index c3a0b34..b857d5c 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -31,6 +31,8 @@ \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace} \newcommand{\RC}[2][inline]{% \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace} +\newcommand{\CER}[2][inline]{% + \todo[color=pink!10,#1]{\sffamily\textbf{CER:} #2}\xspace} \algnewcommand\algorithmicinput{\textbf{Input:}} \algnewcommand\Input{\item[\algorithmicinput]} @@ -121,34 +123,48 @@ at that time. Even if the number of iterations required before the convergence i synchronous case, AIAC 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 numerical applications (synchronous or asynchronous) 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 AIAC 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... 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 AIAC application. The aim of this -paper is twofold. First we give a first approach of the simulation of AIAC algorithms using a simulation tool (i.e. the -SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of asynchronous mode algorithms by comparing their -performance with the synchronous mode. More precisely, we had implemented a program for solving large non-symmetric -linear system of equations by numerical method GMRES (Generalized Minimal Residual) []. We show, that with minor -modifications of the initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a real AIAC -application on different computing architectures. The simulated results we obtained are in line with real results -exposed in ??\AG[]{??}. 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. -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 \np[\%]{40} in asynchronous mode. +Parallel numerical applications (synchronous or asynchronous) 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 AIAC +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 AIAC application. The aim of this paper is twofold. First we give a first +approach of the simulation of AIAC algorithms using a simulation tool (i.e. the +SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of +asynchronous mode algorithms by comparing their performance with the synchronous +mode. More precisely, we had implemented a program for solving large +non-symmetric linear system of equations by numerical method GMRES (Generalized +Minimal Residual) []\AG[]{[]?}.\LZK{Problème traité dans le papier est symétrique ou asymétrique? (Poisson 3D symétrique?)} We show, that with minor modifications of the +initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a +real AIAC application on different computing architectures. The simulated +results we obtained are in line with real results exposed in ??\AG[]{??}. +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. 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 \np[\%]{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 is presented with the settings to create various @@ -184,18 +200,25 @@ in a grid computing context. \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... +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{} -In the context of AIAC algorithms, the use of simulation tools is even more relevant. Indeed, this class of applications -is 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... can lead to very different number of -iterations and so to very different execution times. +\AG{Several works\dots{} what?\\ + Le paragraphe suivant se trouve déjà dans l'intro ?} +In the context of AIAC algorithms, the use of simulation tools is even more +relevant. Indeed, this class of applications is 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. @@ -214,21 +237,23 @@ 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. The SMPI interface supports applications written in C or Fortran, -with little or no modifications. SMPI implements about \np[\%]{80} of the MPI -2.0 standard~\cite{bedaride:hal-00919507}. - -%%% explain simulation -%- simulated processes folded in one real process -%- simulates interactions on the network, fluid model -%- able to skip long-lasting computations -%- traces + visu? - -%%% platforms -%- describe resources and their interconnection, with their properties -%- XML files - -%%% validation + refs +languages. SMPI is the interface that has been used for the work exposed in +this paper. The SMPI interface implements about \np[\%]{80} of the MPI 2.0 +standard~\cite{bedaride:hal-00919507}, and supports applications written in C or +Fortran, with little or no modifications. + +With SimGrid, the execution of a distributed application is simulated on a +single machine. The application code is really executed, but some operations +like the communications are intercepted to be simulated according to the +characteristics of the simulated execution platform. The description of this +target platform is given as an input for the execution, by the mean of an XML +file. It describes the properties of the platform, such as the computing node +with their computing power, the interconnection links with their bandwidth and +latency, and the routing strategy. The simulated running time of the +application is computed according to these properties. + +\AG{Faut-il ajouter quelque-chose ?} +\CER{Comme tu as décrit la plateforme d'exécution, on peut ajouter éventuellement le fichier XML contenant des hosts dans les clusters formant la grille} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Simulation of the multisplitting method} @@ -338,32 +363,27 @@ condition is satisfied \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 $X_l^k$ and $X_l^{k+1}$. -\LZK{Description du processus d'adaptation de l'algo multisplitting à SimGrid} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -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 the six neighbors of each point in a submatrix 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. In synchronous +We did not encounter major blocking problems when adapting the multisplitting algorithm previously described to a simulation environment like SimGrid\LZK[]{SimGrid} unless some code +debugging. Indeed, apart from the review of the program sequence for asynchronous exchanges between the six neighbors of each point (left,right,front,behind,top,down) in a cubic partitionned submatrix within a cluster or between clusters, \LZK{Il faut expliquer pourquoi 6 points voisins (7-point stencil problem)} \CER{J'ai rajouté quelques précisions mais serait-il nécessaire de décrire a ce niveau la discrétisation 3D ?} +the algorithm was executed successfully with SMPI and provided identical outputs as those obtained with direct execution under MPI. In synchronous mode, the execution of the program raised no particular issue but in asynchronous mode, the review of the sequence of MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions -and with the addition of the primitive MPI\_Test was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm. 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. +and with the addition of the primitive MPI\_Test was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm. +\LZK{Peut-être mettre plus de précisions sur les difficultés rencontrées dans la version async et les adaptaions effectuées pour SimGrid}\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} +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 the real life in the simulated environment after the following minor changes. First, all declared global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of -shared memory used by threads simulating each computing units in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had +shared memory used by threads simulating each computing unit in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had also to be reviewed. Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid. 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 tested in synchronous mode with a simulated platform starting from a modest 2 or 3 clusters grid to a larger configuration like simulating -Grid5000 with more than 1500 hosts with 5000 cores~\cite{bolze2006grid}. Once the code debugging and adaptation were complete, the next section shows our methodology and experimental -results. - - - - +Grid5000 with more than 1500 hosts with 5000 cores~\cite{bolze2006grid}.\LZK{Dernière phrase peut être supprimée} \CER {J'ai enlevé la dernière phrase} \section{Experimental results} -When the \emph{real} application runs in the simulation environment and produces the expected results, varying the input +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} @@ -372,18 +392,19 @@ study that the results depend on the following parameters: \item Hosts power (GFlops) can also influence on 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 - \emph{external} precision are critical. They allow to ensure not only the + \textit{external} precision are critical. They allow to ensure not only the convergence of the algorithm but also to get the main objective of the experimentation of the simulation in having an execution time in asynchronous less than in synchronous mode (i.e. speed-up less than 1). \end{itemize} +\LZK{Propositions pour changer le terme ``speedup'': acceleration ratio ou relative gain} A priori, obtaining a speedup less than 1 would be difficult in a local area network configuration where the synchronous mode will take advantage on the rapid exchange of information on such high-speed links. Thus, the methodology adopted was to launch the application on clustered network. In this last configuration, degrading the inter-cluster network performance will -\emph{penalize} the synchronous mode allowing to get a speedup lower than 1. +\textit{penalize} the synchronous mode allowing to get a speedup lower than 1. This action simulates the case of clusters linked with long distance network like Internet. @@ -393,7 +414,8 @@ factors have providing the results shown in Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z = \text{62}$ to 171 elements or from $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} = \text{\np{5211000}}$ entries. - +\LZK{Donner le type et la description du problème traité (problème symétrique Poisson 3D) et préciser peut être aussi qu'on a utilisé un partitionnement 3D} +\CER{Voir ma remarque plus si nécessaire de décrire en détail le partitionnement 3D} % 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 @@ -511,7 +533,7 @@ Table~\ref{tab.cluster.3x67}. \hline speedup & 0.9 \\ \hline - \end{mytable} + \end{mytable} \end{table} Note that the program was run with the following parameters: @@ -533,7 +555,14 @@ lat latency, \dots{}). \item Maximum number of internal and external iterations; \item Internal and external precisions; \item Matrix size $N_x$, $N_y$ and $N_z$; +<<<<<<< HEAD \item Matrix diagonal value: \np{6.0}; + \item Matrix Off-diagonal value: \np{-1}; + \LZK{Off-diagonal values? (-1?)} + \CER{oui} +======= + \item Matrix diagonal value: \np{6.0}, \LZK{Off-diagonal values? (-1.0?)} +>>>>>>> 5fb6769d88c1720b6480a28521119ef010462fa6 \item Execution Mode: synchronous or asynchronous. \end{itemize} @@ -573,6 +602,8 @@ with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was obtained with a bandwidth of \np[Mbit/s]{1} as shown in Table~\ref{tab.cluster.3x67}. +\LZK{Dans le papier, on compare les deux versions synchrone et asycnhrone du multisplitting. Y a t il des résultats pour comparer gmres parallèle classique avec multisplitting asynchrone? Ca permettra de montrer l'intérêt du multisplitting asynchrone sur des clusters distants} + \section{Conclusion} The experimental results on executing a parallel iterative algorithm in asynchronous mode on an environment simulating a large scale of virtual @@ -601,12 +632,13 @@ 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. +\LZK{Perspectives???} + \section*{Acknowledgment} 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 @@ -614,6 +646,8 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX- \bibliographystyle{IEEEtran} \bibliography{IEEEabrv,hpccBib} + + \end{document} %%% Local Variables: