X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/74573ba508001fe69efde16e8f22451de2fa0fec..e4346947aa62df9cab78e792050767d42c5f5ab3:/BookGPU/Chapters/chapter2/ch2.tex?ds=sidebyside

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index ae0704b..c33ac50 100755
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -3,7 +3,9 @@
 \chapter{Introduction to CUDA}
 \label{chapter2}
 
-\section{Introduction}\label{intro}
+\section{Introduction}
+\label{ch2:intro}
+
 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
@@ -12,6 +14,7 @@ example: \cite{ch2:Sanders:2010:CEI}).
 
 
 \section{First example}
+\label{ch2:1ex}
 
 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
@@ -67,15 +70,19 @@ block.
 \lstinputlisting[label=ch2:lst:ex1,caption=A simple example]{Chapters/chapter2/ex1.cu}
 
 \section{Second example: using CUBLAS}
+\label{ch2:2ex}
 
 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
+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
+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.
+simple. Roughly speaking, a reduction operation\index{reduction~operation} is an
+operation  which combines  all the  elements of  an array  and extract  a number
+computed with all the  elements. For example, a sum, a maximum  or a dot product
+are reduction operations. 
 
 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
@@ -104,10 +111,40 @@ 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.
+destination. Then we call the kernel \texttt{addition} which computes the sum of
+all elements of arrays $A$ and $B$. The \texttt{inverse} kernel is called twice,
+once to  inverse elements of array  $C$ and once  for $A$. Finally, we  call the
+function \texttt{cublasDdot} which  computes the dot product of  two vectors. To
+use this routine, we must specify  the handle initialized by Cuda, the number of
+elements to consider,  then each vector is followed by  the offset between every
+element.  After  the  GPU  computation,  it  is  possible  to  check  that  both
+computation produce the same result.
+
+\lstinputlisting[label=ch2:lst:ex2,caption=A simple example with cublas]{Chapters/chapter2/ex2.cu}
+
+\section{Third example: matrix-matrix multiplication}
+\label{ch2:3ex}
+
+
+
+Matrix-matrix multiplication is an operation  which is quite easy to parallelize
+with a GPU. If we consider that  a matrix is represented using a two dimensional
+array,  A[i][j] represents  the  the element  of  the $i^{th}$  row  and of  the
+$j^{th}$ column. In many case, it is easier to manipulate 1D array instead of 2D
+array.   With Cuda,  even if  it is  possible to  manipulate 2D  arrays,  in the
+following we  present an example  based on 1D  array. For sake of  simplicity we
+consider  we  have  a  squared  matrix  of size  \texttt{size}.  So  with  a  1D
+array, \texttt{A[i*size+j]} allows  us to access to the  element of the $i^{th}$
+row and of the $j^{th}$ column.
+
+In sequential the matrix multiplication is performed using three loops. Supposing that $A$, $B$ represent two square matrices, the result of the multiplication of $A \times B$ is 
 
-\lstinputlisting[label=ch2:lst:ex2,caption=A simple example]{Chapters/chapter2/ex2.cu}
+On C2070M Tesla card, this code take 37.68ms to perform the multiplication. On a
+Intel Xeon E31245 at 3.30GHz, it takes 2465ms without any parallelization (using
+only one  core). Consequently the  speed up between  the CPU and GPU  version is
+about 65 which is very good regarding the difficulty of parallelizing this code.
 
+\lstinputlisting[label=ch2:lst:ex3,caption=simple Matrix-matrix multiplication with cuda]{Chapters/chapter2/ex3.cu}
 
 \putbib[Chapters/chapter2/biblio]