From 5f0dd22ada037b4a0657d93645f08e99403867e2 Mon Sep 17 00:00:00 2001 From: laiymani Date: Thu, 17 Apr 2014 10:50:43 +0200 Subject: [PATCH] =?utf8?q?intro=20re-travill=C3=A9e.=20resteencore=20du=20?= =?utf8?q?boulot.=20manque=20des=20refs?= MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit --- hpcc.tex | 70 +++++++++++++++++++++++++++++------------------------ hpccBib.bib | 23 ++++++++++++++++++ 2 files changed, 62 insertions(+), 31 deletions(-) diff --git a/hpcc.tex b/hpcc.tex index 75c02f5..b9b1753 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -117,39 +117,47 @@ demonstrate the convergence of these algorithms \cite{}. Parallelization of such algorithms generally involved the division of the problem into several \emph{pieces} that will be solved in parallel on multiple processing units. The latter will communicate each intermediate results before a new -iteration starts until the approximate solution is reached. These parallel computations can be performed -either in \emph{synchronous} communication mode where a new iteration begin only when all nodes communications are -completed, either \emph{asynchronous} mode where processors can continue independently without or few synchronization -points. - -% DL : reprendre correction ici -Despite the effectiveness of iterative approach, a major drawback of the method is the requirement of huge -resources in terms of computing capacity, storage and high speed communication network. Indeed, limited physical -resources are blocking factors for large-scale deployment of parallel algorithms. - -In recent years, the use of a simulation environment to execute parallel iterative algorithms found some interests in -reducing the highly cost of access to computing resources: (1) for the applications development life cycle and in code -debugging (2) and in production to get results in a reasonable execution time with a simulated infrastructure not -accessible with physical resources. Indeed, the launch of distributed iterative asynchronous algorithms to solve a -given problem on a large-scale simulated environment challenges to find optimal configurations giving the best results -with a lowest residual error and in the best of execution time. According our knowledge, no testing of large-scale -simulation of the class of algorithm solving to achieve real results has been undertaken to date. We had in the scope -of this work implemented a program for solving large non-symmetric linear system of equations by numerical method -GMRES (Generalized Minimal Residual) in the simulation environment SimGrid. The simulated platform had allowed us to -launch the application from a modest computing infrastructure by simulating different distributed architectures -composed by clusters nodes interconnected by variable speed networks. In addition, it has been permitted to show the -effectiveness of asynchronous mode algorithm by comparing its performance with the synchronous mode time. With selected -parameters on the network platforms (bandwidth, latency of inter cluster network) and on the clusters architecture -(number, capacity calculation power) in the simulated environment, the experimental results have demonstrated not only -the algorithm convergence within a reasonable time compared with the physical environment performance, but also a time -saving of up to \np[\%]{40} in asynchronous mode. +iteration starts until the approximate solution is reached. These parallel computations can be performed either in +\emph{synchronous} communication mode where a new iteration begin only when all nodes communications are completed, +either \emph{asynchronous} mode where processors can continue independently without or few synchronization points. For +instance in the \textit{Asynchronous Iterations - Asynchronous Communications (AIAC)} model \cite{bcvc06:ij}, local +computations do not need to wait for required data. Processors can then perform their iterations with the data present +at that time. Even if the number of iterations required before the convergence is generally greater than for the +synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to +synchronizations especially in a grid computing context (see \cite{bcvc06:ij} for more details). + +Parallel numerical applications (synchronous or asynchronous) may have different configuration and deployment +requirements. Quantifying their performance of resource allocation policies and application scheduling algorithms in +grid computing environments under varying load, CPU power and network speeds is very costly, labor intensive and time +consuming \cite{BuRaCa}. The case of AIAC algorithms is even more problematic since they are very sensible to the +execution environment context. For instance, variations in the network bandwith (intra and inter- clusters), in the +number and the power of nodes, in the number of clusters... can lead to very different number of iterations and so to +very different execution times. In this context, it appears that the use of simulation tools to explore various platform +scenarios and to run enormous numbers of experiments quickly can be very promising. In this way, the use of a simulation +environment to execute parallel iterative algorithms found some interests in reducing the highly cost of access to +computing resources: (1) for the applications development life cycle and in code debugging (2) and in production to get +results in a reasonable execution time with a simulated infrastructure not accessible with physical resources. Indeed, +the launch of distributed iterative asynchronous algorithms to solve a given problem on a large-scale simulated +environment challenges to find optimal configurations giving the best results with a lowest residual error and in the +best of execution time. + +To our knowledge, there is no existing work on the large-scale simulation of a real AIAC application. The aim of this +paper is to give a first approach of the simulation of AIAC algorithms using the SimGrid toolkit \cite{SimGrid}. We had +in the scope of this work implemented a program for solving large non-symmetric linear system of equations by numerical +method GMRES (Generalized Minimal Residual). SimGrid had allowed us to launch the application from a modest computing +infrastructure by simulating different distributed architectures composed by clusters nodes interconnected by variable +speed networks. The simulated results we obtained are in line with real results exposed in ?? In addition, it has been +permitted to show the effectiveness of asynchronous mode algorithm by comparing its performance with the synchronous +mode time. With selected parameters on the network platforms (bandwidth, latency of inter cluster network) and on the +clusters architecture (number, capacity calculation power) in the simulated environment, the experimental results have +demonstrated not only the algorithm convergence within a reasonable time compared with the physical environment +performance, but also a time saving of up to \np[\%]{40} in asynchronous mode. This article is structured as follows: after this introduction, the next section will give a brief description of -iterative asynchronous model. Then, the simulation framework SimGrid will be presented with the settings to create -various distributed architectures. The algorithm of the multi-splitting method used by GMRES written with MPI -primitives and its adaptation to SimGrid with SMPI (Simulated MPI) will be in the next section. At last, the experiments -results carried out will be presented before the conclusion which we will announce the opening of our future work after -the results. +iterative asynchronous model. Then, the simulation framework SimGrid is presented with the settings to create various +distributed architectures. The algorithm of the multi-splitting method used by GMRES written with MPI primitives and +its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results +carried out will be presented before some concluding remarks and future works. \section{Motivations and scientific context} diff --git a/hpccBib.bib b/hpccBib.bib index 38ef6e7..c32b4e9 100644 --- a/hpccBib.bib +++ b/hpccBib.bib @@ -34,3 +34,26 @@ year = {1986} key = {MPI}, year = 2014, } + + +@article{BuRaCa, +title = {Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities +}, +author = {Rajkumar Buyya, Rajiv Ranjan and Rodrigo N. Calheiros}, +journal = {Softw: Pract. Exper.}, +volume = {41}, +number = {1}, +pages = {23-50}, +year = {2011} +} + + + +@InProceedings{GueRen, + author = {Guermouche A., Renard H.}, + title = {A first step to the evaluation of SimGrid in the context of a real application}, + booktitle = {Proceedings of the Parallel & Distributed Processing, Workshops Forum (IPDPSW)}, + year = {2010}, + pages = {1--10}, + publisher = {IEEE Computer Society}, +} -- 2.39.5