X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/086d3b0478375bd620c6c5923b8f8b11ad194381..f58a76268a01dff4b9427557dba6f052787415d5:/BookGPU/frontmatter/preface.tex

diff --git a/BookGPU/frontmatter/preface.tex b/BookGPU/frontmatter/preface.tex
index 178e0cc..70eed50 100644
--- a/BookGPU/frontmatter/preface.tex
+++ b/BookGPU/frontmatter/preface.tex
@@ -4,23 +4,24 @@
 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 variety of fields: biology, physics,
-chemisty, phenomon model and prediction, simulation, mathematics, etc.
+chemistry, 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 these applications. Using simulataneously many computing
-cores can significantly speed up the processing time.
+times of these applications. Using  many computing
+cores simulataneously 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 Compute
+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.
+applications although  some alternatives are  available, such as  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
