X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/47cd097a55beb36dbcbbdce56442ca355531d1a8..51c03eb8a38bbfba54b1e2fadca7ab7b1db166b3:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index 8a1dfe8..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. @@ -690,16 +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 @@ -707,22 +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