X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e9cd120f6825d0fd5af216088a20e3af54b7eac0..274dcd73fb827d7abedb68a1c1c356ecda43b622:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index 2434416..e73d2cc 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 @@ -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} @@ -518,7 +518,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ 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 \\ @@ -543,7 +543,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ 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 \\ @@ -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} @@ -692,22 +694,17 @@ elements. \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 allows us to -reach the following three objectives: +reach the following two objectives: \begin{enumerate} \item To have a flexible configurable execution platform that allows us to - simulate asynchronous iterative algorithm for which execution of all parts of - the code is necessary. Using simulations before real execution is a nice - solution to detect the scalability problems. - -\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. + 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} comparing to the synchronous GMRES method +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.