From b9756213bfa3fbf7c5667385f5070b003b9bf0b3 Mon Sep 17 00:00:00 2001 From: raphael couturier Date: Sun, 27 Apr 2014 13:53:07 +0200 Subject: [PATCH] petites modifs --- hpcc.tex | 48 ++++++++++++++++++++++++------------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/hpcc.tex b/hpcc.tex index dabef52..abcf399 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -97,7 +97,7 @@ problems raised by researchers on various scientific disciplines but also by in increasing complexity of these requested applications combined with a continuous increase of their sizes lead to write distributed and parallel algorithms requiring significant hardware resources (grid computing, clusters, broadband network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient -parallel algorithms called \emph{numerical iterative algorithms} executed in a distributed environment. As their name +parallel algorithms called \emph{iterative algorithms} executed in a distributed environment. As their name suggests, these algorithms solve a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value $X_{0}$ 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}. @@ -113,34 +113,34 @@ at that time. Even if the number of iterations required before the convergence i 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{Bahi07} for more details). -Parallel numerical applications (synchronous or asynchronous) may have different -configuration and deployment requirements. Quantifying their resource -allocation policies and application scheduling algorithms in grid computing -environments under varying load, CPU power and network speeds is very costly, -very labor intensive and very time -consuming~\cite{Calheiros:2011:CTM:1951445.1951450}. The case of AIAC -algorithms is even more problematic since they are very sensible to the +Parallel (synchronous or asynchronous) applications may have different +configuration and deployment requirements. Quantifying their resource +allocation policies and application scheduling algorithms in grid computing +environments under varying load, CPU power and network speeds is very costly, +very labor intensive and very time +consuming~\cite{Calheiros:2011:CTM:1951445.1951450}. 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 bandwidth -(intra and inter-clusters), in the number and the power of nodes, in the number -of clusters\dots{} can lead to very different number of iterations and so to -very different execution times. Then, it appears that the use of simulation -tools to explore various platform scenarios and to run large 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 +(intra and inter-clusters), in the number and the power of nodes, in the number +of clusters\dots{} can lead to very different number of iterations and so to +very different execution times. Then, it appears that the use of simulation +tools to explore various platform scenarios and to run large 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 twofold. First we give a first +real AIAC application. There are {\bf two contributions} in this paper. First we give a first approach of the simulation of AIAC algorithms using a simulation tool (i.e. the -SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of -asynchronous mode algorithms by comparing their performance with the synchronous -mode. More precisely, we had implemented a program for solving large +SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of the +asynchronous multisplitting algorithm by comparing its performance with the synchronous +GMRES. More precisely, we had implemented a program for solving large linear system of equations by numerical method GMRES (Generalized Minimal Residual) \cite{ref1}. We show, that with minor modifications of the initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a -- 2.39.5