X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/fd7d6f1c6c149f79839200277703c9ef950150f8..b1fd489e34a8d46d286a0d271c38cbfb442f511f:/BookGPU/Chapters/chapter1/ch1.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index 2fef3a4..bfa57c8 100755
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -59,10 +59,10 @@ have been  proposed. The  other well-known alternative  is OpenCL which  aims at
 proposing an alternative  to CUDA and which is  multiplatform and portable. This
 is a  great advantage since  it is even  possible to execute OpenCL  programs on
 traditional CPUs.  The main drawback is  that it is less close to the hardware
-and  consequently it sometimes  provides  less efficient  programs. Moreover,  CUDA
+and,  consequently, it sometimes  provides  less efficient  programs. Moreover,  CUDA
 benefits from  more mature compilation and optimization  procedures.  Other less
 known environments have been proposed,  but most of them have been discontinued,
-such FireStream by ATI which is  not maintained anymore and has been replaced by
+such as FireStream by ATI, which is  not maintained anymore and has been replaced by
 OpenCL and  BrookGPU  by  Stanford  University~\cite{ch1:Buck:2004:BGS}.   Another
 environment based on  pragma (insertion of pragma directives  inside the code to
 help  the  compiler  to generate  efficient  code)  is  called OpenACC.   For  a
@@ -267,8 +267,8 @@ to fill the shared  memory at the start of the kernel  with global data that are
 used very  frequently, then threads can  access it for  their computation.  Threads
 can obviously change  the content of this shared  memory either with computation
 or by loading  other data and they can  store its content in the  global memory. So
-shared memory can  be seen as a cache memory which is manageable manually. This
-obviously  requires an effort from the programmer.
+shared memory can  be seen as a cache memory, which is manually managed. This
+obviously  requires effort from the programmer.
 
 On  recent cards,  the programmer  may decide  what amount  of cache  memory and
 shared memory is attributed to a kernel. The cache memory is an L1 cache which is