X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/eee2ce96cdbc6990b0a059a92129c0f547a3dad2..fe2acffc0f8d127cdeb32859d2666897c00f0058:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index 37ad4e3..2523d89 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -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 @@ -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.} @@ -493,7 +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.} @@ -523,7 +523,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = power (GFlops) & 1 & 1 & 1 & 1.5 & 1.5 \\ \hline - size $(n^3)$ + size $(N)$ & 62 & 62 & 62 & 100 & 100 \\ \hline Precision @@ -548,7 +548,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = 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 @@ -647,11 +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} @@ -664,7 +664,7 @@ 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} 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 +obtained in asynchronous mode for a matrix size of $62^3$ 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 increasing the matrix size up to 100 elements, it was necessary to increase the CPU power of \np[\%]{50} to \np[GFlops]{1.5} to get the algorithm convergence and the same order of asynchronous mode efficiency. Maintaining such processor power but increasing network throughput inter cluster up to