X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/5b14bd57f8f19b1de6a6dfc514031f6e37dcd99c..51c03eb8a38bbfba54b1e2fadca7ab7b1db166b3:/hpcc.tex?ds=inline diff --git a/hpcc.tex b/hpcc.tex index 8bf89cd..7d96f2b 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 ans simulations allow us to see when the asynchronous multisplitting algorithm can be more efficient than the GMRES one to solve a 3D Poisson problem. @@ -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, @@ -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 @@ -509,7 +508,8 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{150}^\text{3} = \begin{table}[!t] \centering - \caption{2 clusters, each with 50 nodes} + \caption{Relative gain of the multisplitting algorithm compared to GMRES for + different configurations with 2 clusters, each one composed of 50 nodes.} \label{tab.cluster.2x50} \begin{mytable}{5} @@ -630,13 +630,12 @@ Note that the program was run with the following parameters: \begin{itemize} \item HOSTFILE: Text file containing the list of the processors units name. Here 100 hosts; -\item PLATFORM: XML file description of the platform architecture : two clusters (cluster1 and cluster2) with the following characteristics : +\item PLATFORM: XML file description of the platform architecture whith the following characteristics: %two clusters (cluster1 and cluster2) with the following characteristics : \begin{itemize} - \item Processor unit power: \np[GFlops]{1.5}; - \item Intracluster network bandwidth: \np[Gbit/s]{1.25} and latency: - \np[$\mu$s]{0.05}; - \item Intercluster network bandwidth: \np[Mbit/s]{5} and latency: - \np[$\mu$s]{5}; + \item 2 clusters of 50 hosts each; + \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5}; + \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{0.05}; + \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[$\mu$s]{20}; \end{itemize} \end{itemize} @@ -645,11 +644,11 @@ Note that the program was run with the following parameters: \begin{itemize} \item Description of the cluster architecture matching the format ; + clusters> ; \item Maximum number of iterations; \item Precisions on the residual error; \item Matrix size $N_x$, $N_y$ and $N_z$; -\item Matrix diagonal value: $6$ (See~(\ref{eq:03})); +\item Matrix diagonal value: $6$ (See Equation~(\ref{eq:03})); \item Matrix off-diagonal value: $-1$; \item Communication mode: asynchronous. \end{itemize} @@ -658,10 +657,10 @@ 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 @@ -691,16 +690,16 @@ 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 +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: \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 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 @@ -708,22 +707,23 @@ of the cluster and network specifications permitting to save time in executing the algorithm in asynchronous mode. \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 +speeder up to \np[\%]{40} comparing 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