X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/ecd2ddac55172a779e7e26d3bd5b1b2cb95033d6..17d1891ee5feec4f52ef9c51bfa60b78f0bd14c2:/BookGPU/frontmatter/preface.tex?ds=sidebyside

diff --git a/BookGPU/frontmatter/preface.tex b/BookGPU/frontmatter/preface.tex
index a2bd0f2..9fbc466 100644
--- a/BookGPU/frontmatter/preface.tex
+++ b/BookGPU/frontmatter/preface.tex
@@ -1,39 +1,40 @@
+\chapter*{Preface}
+
 
 This book is intended to present the design of significant scientific
 applications on GPUs. Scientific applications require more and more
-computational power in a large vaariety of fields: biology, physics,
-chemisty, phenomon model and prediction, simulation, mathematics, ...
+computational power in a large variety of fields: biology, physics,
+chemisty, phenomon model and prediction, simulation, mathematics, etc.
 
 In order to be able to handle more complex applications, the use of
 parallel architectures is the solution to decrease the execution
-times of theses applications. Using simulataneously many computing
+times of these applications. Using simulataneously many computing
 cores can significantly speed up the processing time.
 
-Nevertheless using parallel architectures is not so easy and has
-always required an endeavor to parallelize an application. Nowadays
-with general purpose graphics processing units (GPGPU), it is possible
-to use either general graphic cards or dedicated graphic cards to benefit from
-the computational power of all the cores available inside these
-cards. The NVidia company introduced CUDA in 2007 to unify the
-programming model to use their video card. CUDA is currently the most
-used environment for designing GPU applications although some
-alternatives are available, for example OpenCL. According to
-applications and the GPU considered, a speed up from 5 up to 50 or even more can be
-expected using a GPU instead of computing with a CPU.
+Nevertheless using parallel architectures is not so easy and has always required
+an  endeavor  to  parallelize  an  application. Nowadays  with  general  purpose
+graphics processing units (GPGPU), it  is possible to use either general graphic
+cards or dedicated graphic cards to  benefit from the computational power of all
+the cores  available inside these  cards. The NVidia company  introduced Compute
+Unified Device Architecture (CUDA) in 2007 to unify the programming model to use
+their video card. CUDA is currently  the most used environment for designing GPU
+applications   although   some   alternatives   are   available,   for   example,
+Open Computing Language (OpenCL). According to  applications and the GPU considered, a speed  up from 5 up
+to 50, or even more can be expected using a GPU over computing with a CPU.
 
 The programming model of GPU is quite different from the one of
 CPU. It is well adapted to data parallelism applications. Several
 books present the CUDA programming models and multi-core applications
 design. This book is only focused on scientific applications on GPUs. It
-contains 19 chapters gathered in 5 parts.
+contains 20 chapters gathered in 6 parts.
 
 The first part presents the GPUs. The second part focuses on two
 significant image processing applications on GPUs. Part three presents
 two general methodologies for software development on GPUs. Part four
-describes three optmitization problems on GPUs. The fifth part, the
-longuest one, presents 7 numerical applications. Finally part six
-illustrates 3 other applications that are not included in the previous
+describes three optimization problems on GPUs. The fifth part, the
+longest one, presents seven numerical applications. Finally part six
+illustrates three other applications that are not included in the previous
 parts.
 
 Some codes presented in this book are available online on my webpage:
-http://members.femto-st.fr/raphael-couturier/gpu-book/. 
+http://members.femto-st.fr/raphael-couturier/gpu-book/