X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e2e7a96acea66b3045f222a94ec5aae79894fe3d..8a9c44cff1e89fecee221a84aa00ac7be4d7b827:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index 318a85e..dc76042 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -82,8 +82,8 @@ what parameters could influence or not the behaviors of an algorithm. In this paper, we show that it is interesting to use SimGrid to simulate the behaviors of asynchronous iterative algorithms. For that, we compare the behaviour of a synchronous GMRES algorithm with an asynchronous multisplitting one with -simulations in which we choose some parameters. Both codes are real MPI -codes. Simulations allow us to see when the multisplitting algorithm can be more +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. @@ -103,7 +103,7 @@ suggests, these algorithms solve a given problem by successive iterations ($X_{n $X_{0}$ 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 involve the division of the problem +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 @@ -228,13 +228,13 @@ In the context of asynchronous algorithms, the number of iterations to reach the convergence depends on the delay of 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 iteratie algorithms comes from the fact it is necessary to run the algorithm +asynchronous iterative algorithms comes from the fact it is necessary to run the algorithm with real data. In fact, from an execution to another 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 clusrters bandwith and latency, ....) and of the -algorithm (number of splitting with the multisplitting algorithm), the -multisplitting code will obtain the solution more or less quickly. Or course, +nodes, inter and intra clusrters bandwith 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 of 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 behaviors of @@ -251,8 +251,8 @@ SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid} is a simulation framework to study the behavior of large-scale distributed systems. As its name says, 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 from 1999, but it's still actively developed and distributed as an open -source software. Today, it's one of the major generic tools in the field of +date 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 @@ -384,7 +384,7 @@ exchanged by message passing using MPI non-blocking communication routines. \begin{figure}[!t] \centering \includegraphics[width=60mm,keepaspectratio]{clustering} -\caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.} +\caption{Example of three distant clusters of processors.} \label{fig:4.1} \end{figure} @@ -395,9 +395,9 @@ processor is designated (for example the processor with rank 1) and masters of all clusters are interconnected by a virtual unidirectional ring network (see Figure~\ref{fig:4.1}). During the resolution, a Boolean token circulates around the virtual ring from a master processor to another until the global convergence -is achieved. So starting from the cluster with rank 1, each master processor $i$ +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 $i+1$. Finally, the +\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 masters of other clusters. In this work, @@ -422,7 +422,7 @@ u =0 \text{~on~} \Gamma =\partial\Omega \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 difference 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 the general expression could be written as +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 the 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), @@ -450,7 +450,7 @@ The parallel solving of the 3D Poisson problem with our multisplitting method re %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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 +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.} @@ -476,15 +476,14 @@ study that the results depend on the following parameters: \item Hosts processors 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 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 in asynchronous communication less than in - synchronous mode. 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. -\end{itemize} + 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 mode will take advantage on the +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 @@ -494,7 +493,7 @@ simulates the case of distant clusters linked with long distance network as in g Both codes were simulated on a two clusters based network with 50 hosts each, totaling 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 $N_x = N_y = N_z = \text{62}$ to 150 elements (that is from +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 in average 2.5 times faster than with the synchronous GMRES one. %\AG{Expliquer comment lire les tableaux.} @@ -509,7 +508,8 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = \begin{table}[!t] \centering - \caption{2 clusters, each with 50 nodes} + \caption{Relative gain of the multisplitting algorithm compared to GMRES for + different configurations with 2 clusters, each one composed of 50 nodes.} \label{tab.cluster.2x50} \begin{mytable}{5} @@ -518,12 +518,12 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = & 5 & 5 & 5 & 5 & 5 \\ \hline latency (ms) - & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\ + & 20 & 20 & 20 & 20 & 20 \\ \hline power (GFlops) & 1 & 1 & 1 & 1.5 & 1.5 \\ \hline - size $(n^3)$ + size $(N)$ & 62 & 62 & 62 & 100 & 100 \\ \hline Precision @@ -543,12 +543,12 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = & 50 & 50 & 50 & 50 & 50 \\ % & 10 & 10 \\ \hline latency (ms) - & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\ % & 0.03 & 0.01 \\ + & 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 $(n^3)$ + size $(N)$ & 110 & 120 & 130 & 140 & 150 \\ % & 171 & 171 \\ \hline Precision @@ -561,13 +561,15 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = \end{mytable} \end{table} +\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la table?} + %Then we have changed the network configuration using three clusters containing %respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the %clusters. In the same way as above, a judicious choice of key parameters has %permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the %relative gains greater than 1 with a matrix size from 62 to 100 elements. -\CER{En accord avec RC, on a pour le moment enlevé les tableaux 2 et 3 sachant que les résultats obtenus sont limites. De même, on a enlevé aussi les deux dernières colonnes du tableau I en attendant une meilleure performance et une meilleure precision} +%\CER{En accord avec RC, on a pour le moment enlevé les tableaux 2 et 3 sachant que les résultats obtenus sont limites. De même, on a enlevé aussi les deux dernières colonnes du tableau I en attendant une meilleure performance et une meilleure precision} %\begin{table}[!t] % \centering % \caption{3 clusters, each with 33 nodes} @@ -634,8 +636,8 @@ Note that the program was run with the following parameters: \begin{itemize} \item 2 clusters of 50 hosts each; \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5}; - \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{0.05}; - \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[$\mu$s]{20}; + \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\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} @@ -645,11 +647,11 @@ Note that the program was run with the following parameters: \begin{itemize} \item Description of the cluster architecture matching the format ; -\item Maximum number of iterations; -\item Precisions on the residual error; +\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 value: $-1$; +\item Matrix diagonal value: $6$ (see Equation~(\ref{eq:03})); +\item Matrix off-diagonal values: $-1$; \item Communication mode: asynchronous. \end{itemize} @@ -657,10 +659,10 @@ Note that the program was run with the following parameters: 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 more than 2.5, showing the effectiveness of the -asynchronous performance compared to the synchronous mode. +asynchronous multisplitting compared to GMRES with two distant clusters. With these settings, Table~\ref{tab.cluster.2x50} shows -that after a deterioration of inter cluster network with a bandwidth of \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power +that after setting the bandwidth of the inter cluster network to \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power of one GFlops, an efficiency of about \np[\%]{40} is obtained in asynchronous mode for a matrix size of 62 elements. It is noticed that the result remains stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By @@ -690,39 +692,35 @@ elements. %\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 experimental results on executing a parallel iterative algorithm in -asynchronous mode on an environment simulating a large scale of virtual -computers organized with interconnected clusters have been presented. -Our work has demonstrated that using such a simulation tool allow us to -reach the following three objectives: +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 allows us to +reach the following two objectives: \begin{enumerate} -\item To have a flexible configurable execution platform resolving the -hard exercise to access to very limited but so solicited physical -resources; -\item to ensure the algorithm convergence with a reasonable time and -iteration number ; -\item and finally and more importantly, to find the correct combination -of the cluster and network specifications permitting to save time in -executing the algorithm in asynchronous mode. +\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 in certain conditions, asynchronous mode is -speeder up to \np[\%]{40} than executing the algorithm in synchronous mode +Our results have shown that with two distant clusters, the asynchronous multisplitting is faster to \np[\%]{40} compared to the synchronous GMRES method which is not negligible for solving complex practical problems with more and more increasing size. - Several studies have already addressed the performance execution time of +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. -\LZK{Perspectives???} +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 experimentally validate our study. \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{}} +%\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