X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/a91615879d7d661f352b74c8d4fa5c883f324acf..2f2f8237094c0e1c2d07da21054aafcc99672647:/hpcc.tex?ds=inline diff --git a/hpcc.tex b/hpcc.tex index 529e9c1..5559af7 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -83,7 +83,7 @@ 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 which let us easily choose some parameters. Both codes are real MPI -codes ans simulations allow us to see when the asynchronous multisplitting algorithm can be more +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 @@ -383,8 +383,8 @@ 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.} + \includegraphics[width=60mm,keepaspectratio]{clustering2} +\caption{Example of two 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}