suite

[book_gpu.git] / BookGPU / Chapters / chapter2 / ch2.tex

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index b06e9be3400ac32d4bc5ae254597feda71d39a40..0640708ecb3fed538952560cf313bc8c5a5e6223 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -9,7 +9,7 @@
 In this chapter  we give some simple examples on CUDA  programming.  The goal is
 not to provide an exhaustive presentation of all the functionalities of CUDA but
 rather giving some basic elements. Of  course, readers that do not know CUDA are
 In this chapter  we give some simple examples on CUDA  programming.  The goal is
 not to provide an exhaustive presentation of all the functionalities of CUDA but
 rather giving some basic elements. Of  course, readers that do not know CUDA are

-invited to read other books that are specialized on CUDA programming.
+invited to read other books that are specialized on CUDA programming (for example: \cite{Sanders:2010:CEI}).

 
 
 \section{First example}
 
 
 \section{First example}


@@ -17,7 +17,7 @@ invited to read other books that are specialized on CUDA programming.
 This first example is  intented to show how to build a  very simple example with
 CUDA.   The goal  of this  example is  to performed  the sum  of two  arrays and
 putting the  result into a  third array.   A cuda program  consists in a  C code
 This first example is  intented to show how to build a  very simple example with
 CUDA.   The goal  of this  example is  to performed  the sum  of two  arrays and
 putting the  result into a  third array.   A cuda program  consists in a  C code

-which calls CUDA kernels that are executed on a GPU.
+which calls CUDA kernels that are executed on a GPU. The listing of this code is in Listing~\ref{ch2:lst:ex1}

 
 
 As GPUs have  their own memory, the first step consists  in allocating memory on
 
 
 As GPUs have  their own memory, the first step consists  in allocating memory on


@@ -41,5 +41,29 @@ parameter is set to  \texttt{cudaMemcpyHostToDevice}. The first parameter of the
 function is the destination array, the  second is the source array and the third
 is the number of elements to copy (exprimed in bytes).
 
 function is the destination array, the  second is the source array and the third
 is the number of elements to copy (exprimed in bytes).
 

-\putbib[biblio]
+Now the GPU contains the data needed to perform the addition. In sequential such
+addition is  achieved out with a  loop on all the  elements.  With a  GPU, it is
+possible to perform  the addition of all elements of the  arrays in parallel (if
+the   number  of   blocks   and   threads  per   blocks   is  sufficient).    In
+Listing\ref{ch2:lst:ex1}     at    the     beginning,    a     simple    kernel,
+called \texttt{addition} is defined to  compute in parallel the summation of the
+two arrays. With CUDA, a  kernel starts with the keyword \texttt{\_\_global\_\_}
+which  indicates that  this  kernel  can be  call  from the  C  code. The  first
+instruction  in  this  kernel  is   used  to  computed  the  \texttt{tid}  which
+representes the  thread index.  This thread  index is computed  according to the
+values    of    the    block    index    (it   is    a    variable    of    CUDA
+called  \texttt{blockIdx\index{CUDA~keywords!blockIdx}}). Blocks of  threads can
+be decomposed into  1 dimension, 2 dimensions or 3  dimensions. According to the
+dimension of data  manipulated, the appropriate dimension can  be useful. In our
+example, only  one dimension  is used.  Then  using notation \texttt{.x}  we can
+access to the first dimension (\texttt{.y} and \texttt{.z} allow respectively to
+access      to      the     second      and      third     dimension).       The
+variable \texttt{blockDim}\index{CUDA~keywords!blockDim} gives  the size of each
+block.
+
+
+
+\lstinputlisting[label=ch2:lst:ex1,caption=A simple example]{Chapters/chapter2/ex1.cu}
+
+\putbib[Chapters/chapter2/biblio]