X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/44b8f845847505b81dc0f1199c49e67a495ed7a0..55ce7168c6e69a2462d76c95dc9a5298ceedb04f:/BookGPU/Chapters/chapter6/Intro.tex?ds=sidebyside

diff --git a/BookGPU/Chapters/chapter6/Intro.tex b/BookGPU/Chapters/chapter6/Intro.tex
index 5a8d0e7..77b314d 100644
--- a/BookGPU/Chapters/chapter6/Intro.tex
+++ b/BookGPU/Chapters/chapter6/Intro.tex
@@ -1,28 +1,28 @@
 \section{Introduction}\label{ch6:intro}
 
-This  chapter  proposes to  draw  several  development  methodologies to  obtain
+This chapter proposes  to draw upon several development  methodologies to obtain
 efficient codes  in classical scientific applications.   Those methodologies are
-based on the  feedback from several research works  involving GPUs, either alone
-in  a  single   machine  or  in  a  cluster  of   machines.   Indeed,  our  past
-collaborations  with industries  have  allowed us  to  point out  that in  their
-economical  context,  they   can  adopt  a  parallel  technology   only  if  its
-implementation  and  maintenance costs  are  small  according  to the  potential
-benefits (performance,  accuracy,...). So, in such contexts,  GPU programming is
-still  regarded  with   some  distance  according  to  its   specific  field  of
-applicability (SIMD/SIMT model) and  its still higher programming complexity and
-maintenance. In the academic domain, things  are a bit different but studies for
-efficiently  integrating GPU  computations in  multi-core clusters  with maximal
-overlapping of  computations with communications and/or  other computations, are
-still rare.
+based on  the feedback from several  research works involving GPUs,  either in a
+single machine  or in  a cluster of  machines.  Indeed, our  past collaborations
+with industries have  allowed us to point out that  in their economical context,
+they can adopt a parallel  technology only if its implementation and maintenance
+costs   are   small  compared   with   the   potential  benefits   (performance,
+accuracy, etc.). So,  in such  contexts, GPU programming  is still  regarded with
+some  distance due  to its  specific  field of  applicability (SIMD/SIMT  model:
+Single  Instruction  Multiple  Data/Thread)  and its  still  higher  programming
+complexity and maintenance.  In the academic domain, things are a bit different,
+but studies  for efficiently integrating GPU computations  in multicore clusters
+with  maximal  overlapping  of  computations with  communications  and/or  other
+computations are still rare.
 
-For these  reasons, the major  aim of  that chapter is  to propose as  simple as
-possible  general  programming patterns  that  can  be  followed or  adapted  in
+For  these  reasons,  the major  aim  of  that  chapter  is to  propose  general
+programming patterns, as simple as possible,  that can be followed or adapted in
 practical implementations of parallel scientific applications.
 % Also, according to  our experience in industrial collaborations,  we propose a
 % small  prospect  analysis about  the  perenity  of  such accelerators  in  the
 % middle/long term.
-Also, we propose in a third part, a prospect analysis together with a particular
-programming tool that is intended to ease multi-core GPU cluster programming.
+In  addition,  we  propose  a  prospect  analysis  together  with  a  particular
+programming tool that is intended to ease multicore GPU cluster programming.
 
 
 %%% Local Variables:
