From 27763a871f64e95ec41f32b22e50761dbd4e5600 Mon Sep 17 00:00:00 2001
From: "baptiste.coudray" <baptiste.coudray@etu.hesge.ch>
Date: Fri, 30 Jul 2021 21:29:04 +0200
Subject: [PATCH] Fixed french

---
 src/text/00-preface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/text/00-preface.md b/src/text/00-preface.md
index 22c765d..587bde3 100644
--- a/src/text/00-preface.md
+++ b/src/text/00-preface.md
@@ -10,7 +10,7 @@ I would like to thank the people who helped me during this project:
 
 # Abstract {-}
 
-Today, most computers are equipped with (+^GPU). They provide more and more computing cores and have become fundamental embedded high-performance computing tools. In this context, the number of applications taking advantage of these tools seems low at first glance. The problem is that the development tools are heterogeneous, complex, and strongly dependent on the (+GPU) running the code. Futhark is an experimental, functional, and architecture agnostic language; that is why it seems relevant to study it.  It allows generating code allowing a standard sequential execution (on a single-core processor), on (+GPU) (with (+CUDA) and (+OpenCL) backends), on several cores of the same processor (shared memory). To make it a tool that could be used on all high-performance platforms, it lacks support for distributed computing with (+MPI). Nous créons une librairie qui effectue la distribution d'un automate cellulaire sur plusieurs noeuds de calculs via MPI. The update of the cellular automaton is computed via the Futhark language using one of the four available backends (sequential, multicore, OpenCL, and CUDA). Pour valider notre librairie, we implement a cellular automaton in one dimension ((+SCA)), in two dimensions (Game of Life) and three dimensions ((+LBM)). Finally, with the performance tests performed, we obtain an ideal speedup in one and two dimensions with the sequential and multicore backend. With the GPU backend, we obtain an ideal speedup only when the number of tasks equals the number of GPUs.
+Today, most computers are equipped with (+^GPU). They provide more and more computing cores and have become fundamental embedded high-performance computing tools. In this context, the number of applications taking advantage of these tools seems low at first glance. The problem is that the development tools are heterogeneous, complex, and strongly dependent on the (+GPU) running the code. Futhark is an experimental, functional, and architecture agnostic language; that is why it seems relevant to study it.  It allows generating code allowing a standard sequential execution (on a single-core processor), on (+GPU) (with (+CUDA) and (+OpenCL) backends), on several cores of the same processor (shared memory). To make it a tool that could be used on all high-performance platforms, it lacks support for distributed computing with (+MPI). We create a library which perform the distribution of a cellular automaton on multiple compute nodes through MPI. The update of the cellular automaton is computed via the Futhark language using one of the four available backends (sequential, multicore, OpenCL, and CUDA). In order to validate our library, we implement a cellular automaton in one dimension ((+SCA)), in two dimensions (Game of Life) and three dimensions ((+LBM)). Finally, with the performance tests performed, we obtain an ideal speedup in one and two dimensions with the sequential and multicore backend. With the GPU backend, we obtain an ideal speedup only when the number of tasks equals the number of GPUs.
 
 \begin{figure} \vspace{.1cm} \begin{center} \includegraphics[scale=0.4]{figs/front-logo.png}
 \end{center} \end{figure} \begin{tabular}{ p{3cm} p{1cm} p{1cm} p{6cm} } \multicolumn{1}{l}{Candidate:}& & &
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