new

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

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index 75be84bc7e690206142d347d57a3eacdca84f642..7fc84710cadc77fb079a3824bdf3b150aac0a281 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -23,16 +23,17 @@ are executed on a GPU. This code is in Listing~\ref{ch2:lst:ex1}.
 
 
 As GPUs have  their own memory, the first step consists  of allocating memory on
 
 
 As GPUs have  their own memory, the first step consists  of allocating memory on

-the   GPU.   A   call   to  \texttt{cudaMalloc}\index{CUDA functions!cudaMalloc}
-allocates memory  on the GPU.  The  second parameter represents the  size of the
-allocated variables, this size is expressed in bits.
+the  GPU.    A  call  to   \texttt{cudaMalloc}\index{CUDA  functions!cudaMalloc}
+allocates memory on  the GPU. {\bf REREAD The first parameter of this  function is a pointer
+on a  memory on the  device, i.e. the  GPU.} The second parameter  represents the
+size of the allocated variables, this size is expressed in bits.

 \pagebreak
 \lstinputlisting[label=ch2:lst:ex1,caption=simple example]{Chapters/chapter2/ex1.cu}
 
 
 In this example, we  want to compare the execution time of  the additions of two
 arrays in  CPU and  GPU. So  for both these  operations, a  timer is  created to
 \pagebreak
 \lstinputlisting[label=ch2:lst:ex1,caption=simple example]{Chapters/chapter2/ex1.cu}
 
 
 In this example, we  want to compare the execution time of  the additions of two
 arrays in  CPU and  GPU. So  for both these  operations, a  timer is  created to

-measure the  time. CUDA proposes to  manipulate timers quite  easily.  The first
+measure the  time. CUDA  manipulates timers quite  easily.  The first

 step is to create the timer\index{CUDA functions!timer}, then to start it, and at
 the end to stop it. For each of these operations a dedicated function is used.
 
 step is to create the timer\index{CUDA functions!timer}, then to start it, and at
 the end to stop it. For each of these operations a dedicated function is used.
 


@@ -64,7 +65,7 @@ CUDA). Blocks of threads and thread  indexes can be decomposed into 1 dimension,
 the dimension of blocks of threads  must be chosen carefully. In our example,  only one dimension is
 used.   Then using the notation  \texttt{.x}, we  can access  the  first dimension
 (\texttt{.y}  and \texttt{.z},  respectively allow access  to the  second and
 the dimension of blocks of threads  must be chosen carefully. In our example,  only one dimension is
 used.   Then using the notation  \texttt{.x}, we  can access  the  first dimension
 (\texttt{.y}  and \texttt{.z},  respectively allow access  to the  second and

-third dimension).   The variable \texttt{blockDim}\index{CUDA keywords!blockDim}
+third dimensions).   The variable \texttt{blockDim}\index{CUDA keywords!blockDim}

 gives the size of each block.
 
 
 gives the size of each block.
 
 


@@ -74,12 +75,12 @@ gives the size of each block.
 \section{Second example: using CUBLAS \index{CUBLAS}}
 \label{ch2:2ex}
 
 \section{Second example: using CUBLAS \index{CUBLAS}}
 \label{ch2:2ex}
 

-The Basic Linear Algebra Subprograms  (BLAS) allows programmers to use efficient
+The Basic Linear Algebra Subprograms  (BLAS) allow programmers to use efficient

 routines for basic linear operations. Those  routines  are heavily  used in  many
 scientific applications  and are optimized for  vector operations, matrix-vector
 operations,                           and                           matrix-matrix
 operations~\cite{ch2:journals/ijhpca/Dongarra02}. Some  of those operations seem
 routines for basic linear operations. Those  routines  are heavily  used in  many
 scientific applications  and are optimized for  vector operations, matrix-vector
 operations,                           and                           matrix-matrix
 operations~\cite{ch2:journals/ijhpca/Dongarra02}. Some  of those operations seem

-to be  easy to  implement with CUDA.   Nevertheless, as  soon as a  reduction is
+to be  easy to  implement with CUDA; however, as  soon as a  reduction is

 needed, implementing an efficient reduction routine with CUDA is far from being
 simple. Roughly speaking, a reduction operation\index{reduction operation} is an
 operation  which combines  all the  elements of  an array  and extracts  a number
 needed, implementing an efficient reduction routine with CUDA is far from being
 simple. Roughly speaking, a reduction operation\index{reduction operation} is an
 operation  which combines  all the  elements of  an array  and extracts  a number


@@ -144,7 +145,7 @@ three loops. We assume that $A$, $B$  represent two square matrices and the
 result   of    the   multiplication    of   $A   \times    B$   is    $C$.   The
 element \texttt{C[i*size+j]} is computed as follows:
 \begin{equation}
 result   of    the   multiplication    of   $A   \times    B$   is    $C$.   The
 element \texttt{C[i*size+j]} is computed as follows:
 \begin{equation}

-C[size*i+j]=\sum_{k=0}^{size-1} A[size*i+k]*B[size*k+j];
+C[size*i+j]=\sum_{k=0}^{size-1} A[size*i+k]*B[size*k+j].

 \end{equation}
 
 In Listing~\ref{ch2:lst:ex3},  the CPU computation  is performed using  3 loops,
 \end{equation}
 
 In Listing~\ref{ch2:lst:ex3},  the CPU computation  is performed using  3 loops,