From: Arnaud Giersch Date: Thu, 7 May 2015 13:14:20 +0000 (+0200) Subject: Typos. X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/4a76c4a8fcc10af92df927a0bebb9a1793d7e1aa?ds=inline;hp=-c Typos. --- 4a76c4a8fcc10af92df927a0bebb9a1793d7e1aa diff --git a/paper.tex b/paper.tex index 0d24694..0b7dc1d 100644 --- a/paper.tex +++ b/paper.tex @@ -136,7 +136,7 @@ are often very important. So, in this context it is difficult to optimize a given application for a given architecture. In this way and in order to reduce the access cost to these computing resources it seems very interesting to use a simulation environment. The advantages are numerous: development life cycle, -code debugging, ability to obtain results quickly~\ldots. In counterpart, the simulation results need to be consistent with the real ones. +code debugging, ability to obtain results quickly\dots{} In counterpart, the simulation results need to be consistent with the real ones. In this paper we focus on a class of highly efficient parallel algorithms called \emph{iterative algorithms}. The parallel scheme of iterative methods is quite @@ -237,7 +237,7 @@ 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 +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