new

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index 9a3f4eb1180739700d4355eb6d6dc428afdfbe3b..ec639cb7cd56676871aa07e17c28cf74c05f23c8 100755 (executable)
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -10,7 +10,7 @@ This chapter introduces the Graphics  Processing Unit (GPU) architecture and all
 the concepts needed to understand how GPUs  work and can be used to speed up the
 execution of some algorithms. First of all this chapter gives a brief history of
 the development of Graphics card until they can be used in order to make general
 the concepts needed to understand how GPUs  work and can be used to speed up the
 execution of some algorithms. First of all this chapter gives a brief history of
 the development of Graphics card until they can be used in order to make general

-purpose computation.
+purpose computation. Then the 

 
 
 
 
 
 


@@ -23,9 +23,9 @@ repetitive and very specific.  Hence,  some manufacturers have produced more and
 more sofisticated video cards, providing 2D accelerations then 3D accelerations,
 then some  light transforms. Video cards  own their own memory  to perform their
 computation.  From  at least two dedaces,  every personnal computer  has a video
 more sofisticated video cards, providing 2D accelerations then 3D accelerations,
 then some  light transforms. Video cards  own their own memory  to perform their
 computation.  From  at least two dedaces,  every personnal computer  has a video

-card which a  simple for desktop computers or  which provides many accelerations
+card which is  simple for desktop computers or  which provides many accelerations

 for game  and/or graphic oriented computers.  In the latter  case, graphic cards
 for game  and/or graphic oriented computers.  In the latter  case, graphic cards

-may be more expensive than the CPU.
+may be more expensive than a CPU.

 
 After 2000,  video cards allowed to apply  arithmetics operations simulatenously
 on a  sequence of  pixels, also  later called stream  processing. In  this case,
 
 After 2000,  video cards allowed to apply  arithmetics operations simulatenously
 on a  sequence of  pixels, also  later called stream  processing. In  this case,


@@ -33,7 +33,7 @@ information of the  pixels (color, location and other  information) are combined
 in   order  to   produce   a  pixel   color   that  can   be   displayed  on   a
 screen.  Simultaneous  computations  are  provided by  shaders  which  calculate
 rendering effects on graphics hardware  with a high degree of flexibility. These
 in   order  to   produce   a  pixel   color   that  can   be   displayed  on   a
 screen.  Simultaneous  computations  are  provided by  shaders  which  calculate
 rendering effects on graphics hardware  with a high degree of flexibility. These

-shaders handles the stream data with pipelines
+shaders handles the stream data with pipelines.

 
 
 Some reasearchers  tried to apply  those operations on other  data, representing
 
 
 Some reasearchers  tried to apply  those operations on other  data, representing


@@ -70,12 +70,13 @@ comparison with OpenCL, interested readers may refer to~\cite{ch1:CMR:12}.
 
 \section{Architecture of current GPUs}
 
 
 \section{Architecture of current GPUs}
 

-Architecure  of current GPUs  is constantly  evolving. Nevertheless  some trends
-remains true  through this evolution. Processing  units composing a  GPU are far
-more simpler  than a  traditional CPU but  it is  much easier to  integrate many
-computing units inside a  GPU card than many cores inside a  CPU. This is due to
-the fact that cores  of a GPU a simpler than cores of a  CPU.  In 2012, the most
-powerful  GPUs own  more  than  500 cores  and  the most  powerful  CPUs have  8
+Architecture  \index{Architecture  of  a  GPU}  of current  GPUs  is  constantly
+evolving.    Nevertheless    some    trends    remains   true    through    this
+evolution.  Processing  units  composing a  GPU  are  far  more simpler  than  a
+traditional CPU but it is much easier to integrate many computing units inside a
+GPU card than many  cores inside a CPU. This is due to the  fact that cores of a
+GPU are simpler than  cores of a CPU.  In 2012, the  most powerful GPUs own more
+than     500    cores     and    the     most    powerful     CPUs     have    8

 cores. Figure~\ref{ch1:fig:comparison_cpu_gpu} shows  the number of cores inside
 a  CPU  and  inside a  GPU.   In  fact,  in  a  current NVidia  GPU,  there  are
 multiprocessors which have 32 cores (for example on Fermi cards). The core clock
 cores. Figure~\ref{ch1:fig:comparison_cpu_gpu} shows  the number of cores inside
 a  CPU  and  inside a  GPU.   In  fact,  in  a  current NVidia  GPU,  there  are
 multiprocessors which have 32 cores (for example on Fermi cards). The core clock


@@ -220,18 +221,19 @@ explicit that.
 
 \section{Memory hierarchy}
 
 
 \section{Memory hierarchy}
 

-The memory  hierarchy of GPUs  is different from  the one of CPUs.  In practice,
-there is registers, local memory, shared memory, cache memroy and global memory.
+The memory hierarchy of GPUs\index{Memory  hierarchy of a GPU} is different from
+the CPUs  one.  In  practice, there are  registers, local memory,  shared memory,
+cache memroy and global memory.

 
 As  previously  mentioned each  thread  can access  its  own  registers.  It  is
 important to keep in mind that the  number of registers per block is limited. On
 recent cards,  this number is  limited to 64Kb  per SM.  Access to  registers is
 very fast, so when possible it is a good idea to use them.
 
 
 As  previously  mentioned each  thread  can access  its  own  registers.  It  is
 important to keep in mind that the  number of registers per block is limited. On
 recent cards,  this number is  limited to 64Kb  per SM.  Access to  registers is
 very fast, so when possible it is a good idea to use them.
 

-Likewise each thread can access local  memory which in practice much slower than
-registers.  In practice, local memory is automatically used by the compiler when
-all the  registers are  occupied. So  the best idea  is to  optimize the  use of
-registers even if this implies to reduce the number of threads per block. 
+Likewise each thread can access local  memory which, in practice, is much slower
+than registers.  Local memory is automatically used by the compiler when all the
+registers are  occupied. So the  best idea is  to optimize the use  of registers
+even if this implies to reduce the number of threads per block.

 
 Shared memory allows  cooperation between threads of the  same block.  This kind
 of memory  is fast by  it requires  to be manipulated  manually and its  size is
 
 Shared memory allows  cooperation between threads of the  same block.  This kind
 of memory  is fast by  it requires  to be manipulated  manually and its  size is

diff --git a/BookGPU/Chapters/chapter2/ch2.tex b/BookGPU/Chapters/chapter2/ch2.tex
index 0640708ecb3fed538952560cf313bc8c5a5e6223..bff6d44679ddc4a196761befae7bc5ce3b93b075 100755 (executable)
--- a/BookGPU/Chapters/chapter2/ch2.tex
+++ b/BookGPU/Chapters/chapter2/ch2.tex
@@ -52,7 +52,7 @@ 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
 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

-called  \texttt{blockIdx\index{CUDA~keywords!blockIdx}}). Blocks of  threads can
+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
 example, only  one dimension  is used.  Then  using notation \texttt{.x}  we 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