new

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

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index 8222660b03fb33730f512cf2c4c726539ab24fdb..68c309a5be21610517eea9c11f5b45b26ac209a0 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -1,99 +1,97 @@
-\chapterauthor{Raphaël Couturier}{Femto-ST Institute, University of Franche-Comte}
+\chapterauthor{Raphaël Couturier}{Femto-ST Institute, University of Franche-Comte, France}

 
 

-\chapter{Introduction to Cuda}
+\chapter{Introduction to CUDA}

 \label{chapter2}
 
 \section{Introduction}
 \label{ch2:intro}
 
 \label{chapter2}
 
 \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
-invited  to read  other  books that  are  specialized on  Cuda programming  (for
-example: \cite{ch2:Sanders:2010:CEI}).
+In this chapter  we give some simple examples of CUDA  programming.  The goal is
+not to provide an exhaustive presentation of all the functionalities of CUDA but
+rather to give some basic elements. Of  course, readers who do not know CUDA are
+invited  to read  other  books that  are  specialized on  CUDA programming  (for
+example, \cite{ch2:Sanders:2010:CEI}).

 
 
 \section{First example}
 \label{ch2:1ex}
 
 
 
 \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 perform  the sum  of two  arrays and
-put 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}.
+This first example is  intented to show how to build a  very simple program with
+CUDA.  Its goal  is to perform the sum  of two arrays and put the  result into a
+third array.  A CUDA program consists in  a C code which calls CUDA kernels that
+are executed on a GPU. This code is in Listing~\ref{ch2:lst:ex1}.

 
 
 
 

-As GPUs have  their own memory, the first step consists  in allocating memory on
-the GPU.  A call to  \texttt{cudaMalloc}\index{Cuda~functions!cudaMalloc} allows
-to allocate memory on the GPU. The first parameter of this function is a pointer
-on a memory on the device (i.e. the GPU). In this example, \texttt{d\_} is added
-on each variable allocated  on the GPU, meaning this variable is  on the GPU. The
-second parameter represents the size of the allocated variables, this size is in
-bits.
+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.
+
+\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
 
 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
-the end to stop it. For each of these operations a dedicated functions is used.
-
-In  order to  compute  the same  sum  with a  GPU, the  first  step consists  in
-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} allows to
-copy the content of an array allocated in the host to the device when the fourth
+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
+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
 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
 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
+source  array, and  the third  is the  number of  elements to  copy  (expressed in

 bytes).
 
 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  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,
+Now the  GPU contains the  data needed to  perform the addition.   In sequential
+programming, such  addition is  achieved   with a loop  on all  the elements.
+With a GPU,  it is possible to perform  the addition of all the  elements of the
+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
 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
+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
 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{blockIdx}\index{Cuda~keywords!threadIdx}   in
-Cuda). Blocks of threads and thread  indexes 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}
+(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,
+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
+third dimension).   The variable \texttt{blockDim}\index{CUDA keywords!blockDim}

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

-\lstinputlisting[label=ch2:lst:ex1,caption=A simple example]{Chapters/chapter2/ex1.cu}

 
 

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

 \label{ch2:2ex}
 
 The Basic Linear Algebra Subprograms  (BLAS) allows programmers to use efficient
 \label{ch2:2ex}
 
 The Basic Linear Algebra Subprograms  (BLAS) allows programmers to use efficient

-routines  that are  often  required. Those  routines  are heavily  used in  many
-scientific  applications   and  are   very  optimized  for   vector  operations,
-matrix-vector              operations              and             matrix-matrix
+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
 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. 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
+to be  easy to  implement with CUDA.   Nevertheless, 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
+computed from all the  elements. For example, a sum, a maximum,  or a dot product

 are reduction operations.
 
 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
+In this second example,   we have two vectors $A$ and $B$. First
+of all, we want to compute the sum  of both vectors and store the result in a vector $C$. Then we want

 to compute the  scalar product between $1/C$ and $1/A$. This  is just an example
 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.
+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
+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.
 
 addition  of two  arrays  is exactly  the same  as  the one  described in  the
 previous example.
 


@@ -102,29 +100,29 @@ 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
 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
+example.  First,  the user is  asked to define  the number of elements.   Then a
+call  to \texttt{cublasCreate}  initializes  the  CUBLAS library.   It
+creates a 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.  The CPU
+computation is performed  and the time for this  is measured. In
+order to  compute the same result  for 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
 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. 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}
+CUBLAS   function   \texttt{cublasSetVector}.    This   function   has   several
+arguments. More precisely, the first  argument represents the number of elements
+to transfer, the second arguments is the size of each element, 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. 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 computations produce the same result.
+
+\lstinputlisting[label=ch2:lst:ex2,caption=simple example with CUBLAS]{Chapters/chapter2/ex2.cu}

 
 \section{Third example: matrix-matrix multiplication}
 \label{ch2:3ex}
 
 \section{Third example: matrix-matrix multiplication}
 \label{ch2:3ex}


@@ -132,69 +130,69 @@ computation produce the same result.
 
 
 Matrix-matrix multiplication is an operation  which is quite easy to parallelize
 
 
 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.
-
-With  a sequential  programming, the  matrix multiplication  is  performed using
-three loops. Supposing that $A$, $B$  represent two square matrices and that the
+with a GPU. If we consider that  a matrix is represented using a two-dimensional
+array, $A[i][j]$ represents the element of  the $i$ row and of the $j$
+column. In  many cases, it is  easier to manipulate a  one-dimentional (1D) array rather than a 2D
+array.   With CUDA,  even if  it is  possible to  manipulate 2D  arrays,  in the
+following we present an example based on a 1D array. For the sake of simplicity,
+we  consider we  have  a square  matrix of  size  \texttt{size}.  So  with a  1D
+array,  \texttt{A[i*size+j]} allows  us to  have access  to the  element  of the
+$i$ row and of the $j$ column.
+
+With  sequential  programming, the  matrix-matrix multiplication  is  performed using
+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[i*size+j]=\sum_{k=0}^{size-1} A[i*size+k]*B[k*size+j];
+C[size*i+j]=\sum_{k=0}^{size-1} A[size*i+k]*B[size*k+j];

 \end{equation}
 
 \end{equation}
 

-In  Listing~\ref{ch2:lst:ex3}, in  the CPU  computation,  this part  of code  is
-performed using 3 loops, one for $i$, one  for $j$ and one for $k$.  In order to
-perform the same computation on a  GPU, a naive solution consists in considering
-that the matrix $C$ is split into  2 dimensional blocks.  The size of each block
-must be chosen such  as the number of threads per block  is inferior to $1,024$.
+In Listing~\ref{ch2:lst:ex3},  the CPU computation  is performed using  3 loops,
+one  for $i$,  one for  $j$,  and one  for $k$.   In  order to  perform the  same
+computation on a  GPU, a naive solution consists of  considering that the matrix
+$C$ is split into  2-dimensional blocks.  The size of each  block must be chosen
+such that the number of threads per block is less than $1,024$.

 
 
 In Listing~\ref{ch2:lst:ex3},  we consider that  a block contains 16  threads in
 each   dimension,  the   variable  \texttt{width}   is  used   for   that.   The
 
 
 In Listing~\ref{ch2:lst:ex3},  we consider that  a block contains 16  threads in
 each   dimension,  the   variable  \texttt{width}   is  used   for   that.   The

-variable \texttt{nbTh} represents the number of threads per block. So to be able
+variable \texttt{nbTh} represents the number of threads per block. So, 

 to compute the matrix-matrix product on a GPU, each block of threads is assigned
 to compute the matrix-matrix product on a GPU, each block of threads is assigned

-to compute the result  of the product for the elements of  this block.  The main
+to compute the result  of the product of the elements of  that block.  The main

 part of the code is quite similar to the previous code.  Arrays are allocated in
 the  CPU and  the GPU.   Matrices $A$  and $B$  are randomly  initialized.  Then
 part of the code is quite similar to the previous code.  Arrays are allocated in
 the  CPU and  the GPU.   Matrices $A$  and $B$  are randomly  initialized.  Then

-arrays are  transfered inside the  GPU memory with call  to \texttt{cudaMemcpy}.
+arrays are  transferred to the  GPU memory with call  to \texttt{cudaMemcpy}.

 So the first step for each thread of a block is to compute the corresponding row
 So the first step for each thread of a block is to compute the corresponding row

-and   column.    With   a    2   dimensional   decomposition,   \texttt{int   i=
+and   column.    With   a   2-dimensional   decomposition,   \texttt{int   i=

 blockIdx.y*blockDim.y+ threadIdx.y;} allows us to compute the corresponding line
 and  \texttt{int  j=   blockIdx.x*blockDim.x+  threadIdx.x;}  the  corresponding
 blockIdx.y*blockDim.y+ threadIdx.y;} allows us to compute the corresponding line
 and  \texttt{int  j=   blockIdx.x*blockDim.x+  threadIdx.x;}  the  corresponding

-column. Then each  thread has to compute the  sum of the product of  the line of
-$A$   per   the  column   of   $B$.    In  order   to   use   a  register,   the
+column. Then each  thread has to compute the  sum of the product of  the row of
+$A$   by   the  column   of   $B$.    In  order   to   use   a  register,   the

 kernel  \texttt{matmul}  uses a  variable  called  \texttt{sum}  to compute  the
 sum. Then the result is set into  the matrix at the right place. The computation
 of  CPU matrix-matrix multiplication  is performed  as described  previously.  A
 kernel  \texttt{matmul}  uses a  variable  called  \texttt{sum}  to compute  the
 sum. Then the result is set into  the matrix at the right place. The computation
 of  CPU matrix-matrix multiplication  is performed  as described  previously.  A

-timer measures  the time.   In order to  use 2 dimensional  blocks, \texttt{dim3
+timer measures  the time.   In order to  use 2-dimensional  blocks, \texttt{dim3

 dimGrid(size/width,size/width);} allows us  to create \texttt{size/width} blocks
 in each  dimension.  Likewise,  \texttt{dim3 dimBlock(width,width);} is  used to
 create \texttt{width} thread  in each dimension. After that,  the kernel for the
 matrix  multiplication is  called. At  the end  of the  listing, the  matrix $C$
 dimGrid(size/width,size/width);} allows us  to create \texttt{size/width} blocks
 in each  dimension.  Likewise,  \texttt{dim3 dimBlock(width,width);} is  used to
 create \texttt{width} thread  in each dimension. After that,  the kernel for the
 matrix  multiplication is  called. At  the end  of the  listing, the  matrix $C$

-computed by the GPU is transfered back  in the CPU and we check if both matrices
+computed by the GPU is transferred back  into the CPU and we check that both matrices

 C computed by the CPU and the GPU are identical with a precision of $10^{-4}$.
 
 
 C computed by the CPU and the GPU are identical with a precision of $10^{-4}$.
 
 

-On C2070M Tesla card, this code take $37.68$ms to perform the multiplication. On
-a Intel Xeon E31245 at  $3.30$GHz, it takes $2465$ms 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.
+With $1,024  \times 1,024$ matrices,  on a C2070M  Tesla card, this  code takes
+$37.68$ms to perform the multiplication. With an Intel Xeon E31245 at $3.30$GHz, it
+takes $2465$ms  without any parallelization (using only  one core). Consequently
+the speed up  between the CPU and GPU  version is about $65$ which  is very good
+considering the difficulty of parallelizing this code.

 
 

-\lstinputlisting[label=ch2:lst:ex3,caption=simple Matrix-matrix multiplication with cuda]{Chapters/chapter2/ex3.cu}
+\lstinputlisting[label=ch2:lst:ex3,caption=simple matrix-matrix multiplication with cuda]{Chapters/chapter2/ex3.cu}

 
 \section{Conclusion}
 
 \section{Conclusion}

-In this chapter  3 simple Cuda examples have been  presented. Those examples are
-quite  simple  and  they  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 is 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.

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