From 7b4270447fd83944409cefebd66f39b4bd1da6f0 Mon Sep 17 00:00:00 2001 From: David Laiymani Date: Thu, 7 May 2015 11:17:57 +0200 Subject: [PATCH 1/1] DL : modif partie asynchrone : ajout des motivations --- paper.tex | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/paper.tex b/paper.tex index 34f7ec7..31fd190 100644 --- a/paper.tex +++ b/paper.tex @@ -192,7 +192,7 @@ experimental results are presented in section~\ref{sec:expe} followed by some concluding remarks and perspectives. -\section{The asynchronous iteration model} +\section{The asynchronous iteration model and the motivations of our work} \label{sec:asynchro} Asynchronous iterative methods have been studied for many years theoritecally and @@ -216,6 +216,21 @@ point. In the asynchronous model, the convergence detection is more tricky as it must not synchronize all the processors. Interested readers can consult~\cite{myBCCV05c,bahi07,ccl09:ij}. +The number of iterations required to reach the convergence is generally greater +for the asynchronous scheme (this number depends depends on the delay of the +messages). Note that, it is not the case in the synchronous mode where the +number of iterations is the same than in the sequential mode. In this way, the +set of the parameters of the platform (number of nodes, power of nodes, +inter and intra clusters bandwidth and latency \ldots) and of the +application can drastically change the number of iterations required to get the +convergence. It follows that asynchronous iterative algorithms are difficult to +optimize since the financial and deployment costs on large scale multi-core +architecture are often very important. So, prior to delpoyment and tests it +seems very promising to be able to simulate the behavior of asynchronous +iterative algorithms. The problematic is then to show that the results produce +by simulation are in accordance with reality i.e. of the same order of +magnitude. To our knowledge, there is no study on this problematic. + \section{SimGrid} \label{sec:simgrid} @@ -529,7 +544,7 @@ and 4x8). We can observ the low sensitivity of the Krylov multisplitting in the grid: in average, the GMRES (resp. Multisplitting) algorithm performs $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors. -\subsubsection{Running on two different inter-clusters network speeds \\} +\subsubsection{Running on two different inter-clusters network speeds \\} \begin{table} [ht!] \begin{center} @@ -550,7 +565,7 @@ speed inter-cluster network (N1) and also on a less performant network (N2). Figure~\ref{fig:02} shows that end users will gain to reduce the execution time for both algorithms in using a grid architecture like 4x16 or 8x8: the performance was increased by a factor of $2$. The results depict also that when -the network speed drops down (variation of 12.5\%), the difference between the two Multisplitting algorithms execution times can reach more than 25\%. +the network speed drops down (variation of 12.5\%), the difference between the two Multisplitting algorithms execution times can reach more than 25\%. %\RC{c'est pas clair : la différence entre quoi et quoi?} %\DL{pas clair} %\RCE{Modifie} -- 2.39.5