fin correct ch14

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

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index 9c6d0def10d3bdff5fee0604f9e4e40e532d46f5..7fc84710cadc77fb079a3824bdf3b150aac0a281 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -23,24 +23,25 @@ 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
 \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
-step is to create the timer\index{CUDA~functions!timer}, then to start it, and at
+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.
 
 In  order to  compute  the same  sum  with a  GPU, the  first  step consists  of
 transferring the data from the CPU (considered as the host with CUDA) to the GPU
 (considered as the  device with CUDA).  A call  to \texttt{cudaMemcpy} copies the content of an array allocated in the host to the device when the fourth
 parameter                                 is                                 set
 the end to stop it. For each of these operations a dedicated function is used.
 
 In  order to  compute  the same  sum  with a  GPU, the  first  step consists  of
 transferring the data from the CPU (considered as the host with CUDA) to the GPU
 (considered as the  device with CUDA).  A call  to \texttt{cudaMemcpy} copies the content of an array allocated in the host to the device when the fourth
 parameter                                 is                                 set

-to  \texttt{cudaMemcpyHostToDevice}\index{CUDA~functions!cudaMemcpy}.  The first
+to  \texttt{cudaMemcpyHostToDevice}\index{CUDA functions!cudaMemcpy}.  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  (expressed in
 bytes).
 parameter of the function is the  destination array, the second is the
 source  array, and  the third  is the  number of  elements to  copy  (expressed in
 bytes).


@@ -52,36 +53,36 @@ two  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
 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\_\_}   \index{CUDA~keywords!\_\_shared\_\_}   which
+keyword   \texttt{\_\_global\_\_}   \index{CUDA keywords!\_\_shared\_\_}   which

 indicates that this kernel can be called from the C code.  The first instruction
 in this kernel is used to compute the variable \texttt{tid} which represents the
 indicates that this kernel can be called from the C code.  The first instruction
 in this kernel is used to compute the variable \texttt{tid} which represents the

-thread index.   This thread index\index{thread  index} is computed  according to
+thread index.   This thread index\index{CUDA keywords!thread  index} is computed  according to

 the           values            of           the           block           index
 the           values            of           the           block           index

-(called  \texttt{blockIdx} \index{CUDA~keywords!blockIdx}  in CUDA)  and  of the
-thread   index   (called   \texttt{threadIdx}\index{CUDA~keywords!threadIdx}   in
+(called  \texttt{blockIdx} \index{CUDA keywords!blockIdx}  in CUDA)  and  of the
+thread   index   (called   \texttt{threadIdx}\index{CUDA keywords!threadIdx}   in

 CUDA). Blocks of threads and thread  indexes can be decomposed into 1 dimension,
 CUDA). Blocks of threads and thread  indexes can be decomposed into 1 dimension,

-2 dimensions, or  3 dimensions. {\bf A REGARDER} According to the  dimension of manipulated data,
-the appropriate dimension  can be useful. In our example,  only one dimension is
+2 dimensions, or  3 dimensions.  According to the  dimension of manipulated data,
+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
 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.
 
 
 
 
 

-\section{Second example: using CUBLAS}
+\section{Second example: using CUBLAS \index{CUBLAS}}

 \label{ch2:2ex}
 
 \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
 needed, implementing an efficient reduction routine with CUDA is far from being

-simple. Roughly speaking, a reduction operation\index{reduction~operation} is an
+simple. Roughly speaking, a reduction operation\index{reduction operation} is an

 operation  which combines  all the  elements of  an array  and extracts  a number
 computed from all the  elements. For example, a sum, a maximum,  or a dot product
 are reduction operations.
 operation  which combines  all the  elements of  an array  and extracts  a number
 computed from all the  elements. For example, a sum, a maximum,  or a dot product
 are reduction operations.


@@ -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,


@@ -189,10 +190,10 @@ considering the difficulty of parallelizing this code.
 \lstinputlisting[label=ch2:lst:ex3,caption=simple matrix-matrix multiplication with cuda]{Chapters/chapter2/ex3.cu}
 
 \section{Conclusion}
 \lstinputlisting[label=ch2:lst:ex3,caption=simple matrix-matrix multiplication with cuda]{Chapters/chapter2/ex3.cu}
 
 \section{Conclusion}

-In this chapter, three simple CUDA examples have been  presented. They are
-quite  simple. As we  cannot  present  all the  possibilities  of  the  CUDA
-programming, interested  readers  are  invited  to  consult  CUDA  programming
-introduction books if some issues regarding the CUDA programming are not clear.
+In this  chapter, three simple CUDA  examples have been presented.  As we cannot
+present all  the possibilities of  the CUDA programming, interested  readers are
+invited to consult CUDA programming  introduction books if some issues regarding
+the CUDA programming are not clear.

 
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 \putbib[Chapters/chapter2/biblio]