new

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

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index bff6d44679ddc4a196761befae7bc5ce3b93b075..ae0704b99da0385b8e934d872795f131b465290c 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -1,6 +1,4 @@
-\chapterauthor{Author Name1}{Affiliation text1}
-\chapterauthor{Author Name2}{Affiliation text2}
-
+\chapterauthor{Raphaël Couturier}{Femto-ST Institute, University of Franche-Comte}

 
 \chapter{Introduction to CUDA}
 \label{chapter2}
 
 \chapter{Introduction to CUDA}
 \label{chapter2}


@@ -9,7 +7,8 @@
 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 (for example: \cite{Sanders:2010:CEI}).
+invited  to read  other  books that  are  specialized on  CUDA programming  (for
+example: \cite{ch2:Sanders:2010:CEI}).

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


@@ -17,7 +16,8 @@ invited to read other books that are specialized on CUDA programming (for exampl
 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. The listing of this code is in Listing~\ref{ch2:lst:ex1}
+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


@@ -41,17 +41,18 @@ 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).
 

-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
+Now that 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
 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
+two     arrays.     With     CUDA,      a     kernel     starts     with     the
+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
+thread index.   This thread index\index{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
 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


@@ -65,5 +66,48 @@ block.
 
 \lstinputlisting[label=ch2:lst:ex1,caption=A simple example]{Chapters/chapter2/ex1.cu}
 
 
 \lstinputlisting[label=ch2:lst:ex1,caption=A simple example]{Chapters/chapter2/ex1.cu}
 

+\section{Second example: using CUBLAS}
+
+The Basic Linear Algebra Subprograms  (BLAS) allows programmer to use performant
+routines that are often used. Those routines are heavily used in many scientific
+applications  and  are  very   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
+needed, implementing an efficient reduction routines with CUDA is far from being
+simple.
+
+In this second example, we consider that  we have two vectors $A$ and $B$. First
+of all, we want to compute the sum  of both vectors in a vector $C$. Then we want
+to compute the  scalar product between $1/C$ and $1/A$. This  is just an example
+which has no direct interest except to show how to program it with CUDA.
+
+Listing~\ref{ch2:lst:ex2} shows this example with CUDA. The first kernel for the
+addition  of two  arrays  is exactly  the same  as  the one  described in  the
+previous example.
+
+The  kernel  to  compute the  inverse  of  the  elements  of  an array  is  very
+simple. For  each thread index,  the inverse of  the array replaces  the initial
+array.
+
+In the main function,  the beginning is very similar to the  one in the previous
+example.   First, the number  of elements  is asked  to the  user.  Then  a call
+to \texttt{cublasCreate} allows to initialize  the cublas library. It creates an
+handle. Then all the arrays are allocated  in the host and the device, as in the
+previous  example.  Both  arrays  $A$ and  $B$  are initialized.   Then the  CPU
+computation is performed  and the time for this CPU  computation is measured. In
+order to  compute the same result  on the GPU, first  of all, data  from the CPU
+need to be  copied into the memory of  the GPU. For that, it is  possible to use
+cublas function \texttt{cublasSetVector}.  This function several arguments. More
+precisely, the first argument represents the number of elements to transfer, the
+second arguments is the size of  each elements, the third element represents the
+source of the  array to transfer (in  the GPU), the fourth is  an offset between
+each element of  the source (usually this value  is set to 1), the  fifth is the
+destination (in the GPU)  and the last is an offset between  each element of the
+destination.
+
+\lstinputlisting[label=ch2:lst:ex2,caption=A simple example]{Chapters/chapter2/ex2.cu}
+
+

 \putbib[Chapters/chapter2/biblio]
 
 \putbib[Chapters/chapter2/biblio]