suite

[book_gpu.git] / BookGPU / Chapters / chapter1 / ch1.tex

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index ee767b15f7171fc33ddb449a71b0c85719663730..b15918e453dd568c278db035ea6bdb08fe78f5ba 100755 (executable)
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -9,12 +9,12 @@
 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
 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
-purpose computation.  Then  the architecture of a GPU  is illustrated. There are
-many fundamental differences  between a GPU and a  tradition processor. In order
-to benefit from the power of a GPU, a CUDA programmer needs to use threads. They
-have  some particularities  which enable  the CUDA  model to  be  efficient and
-scalable when some constraints are addressed.
+the development  of Graphics  card until they  have been  used in order  to make
+general   purpose   computation.    Then   the   architecture  of   a   GPU   is
+illustrated.  There  are  many  fundamental  differences between  a  GPU  and  a
+tradition  processor. In  order  to benefit  from the  power  of a  GPU, a  CUDA
+programmer needs to use threads. They have some particularities which enable the
+CUDA model to be efficient and scalable when some constraints are addressed.

 
 
 
 
 
 


@@ -24,14 +24,14 @@ Video  cards or Graphics  cards have  been introduced  in personal  computers to
 produce  high quality graphics  faster than  classical Central  Processing Units
 (CPU) and  to alleviate CPU from this  task. In general, display  tasks are very
 repetitive and very specific.  Hence,  some manufacturers have produced more and
 produce  high quality graphics  faster than  classical Central  Processing Units
 (CPU) and  to alleviate CPU from this  task. In general, display  tasks are very
 repetitive and very specific.  Hence,  some manufacturers have produced more and

-more sofisticated video cards, providing 2D accelerations then 3D accelerations,
+more sophisticated video cards, providing 2D accelerations then 3D accelerations,

 then some  light transforms. Video cards  own their own memory  to perform their
 then some  light transforms. Video cards  own their own memory  to perform their

-computation.  For at least two dedaces, every personnal computer has had a video
+computation.  For at least two decades, every personal computer has had a video

 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
 may be more expensive than a CPU.
 
 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
 may be more expensive than a CPU.
 

-Since  2000, video  cards have  allowed  users to  apply arithmetics  operations
+Since  2000, video  cards have  allowed  users to  apply arithmetic  operations

 simultaneously on a sequence of  pixels, also later called stream processing. In
 this case, the 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.
 simultaneously on a sequence of  pixels, also later called stream processing. In
 this case, the 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.


@@ -60,11 +60,11 @@ graphics processing unit (GPGPU)  computing.  Of course other programming models
 have been  proposed. The  other well-known alternative  is OpenCL which  aims at
 proposing an alternative to Cuda  and which is multi-platform and portable. This
 is a  great advantage since  it is even  possible to execute OpenCL  programs on
 have been  proposed. The  other well-known alternative  is OpenCL which  aims at
 proposing an alternative to Cuda  and which is multi-platform and portable. This
 is a  great advantage since  it is even  possible to execute OpenCL  programs on

-traditionnal CPUs.  The main drawback is that it is less tight with the hardware
+traditional CPUs.  The main drawback is that it is less tight with the hardware

 and  consequently sometimes  provides  less efficient  programs. Moreover,  Cuda
 benefits from  more mature compilation and optimization  procedures.  Other less
 known environments  have been proposed, but  most of them have  been stopped, for
 and  consequently sometimes  provides  less efficient  programs. Moreover,  Cuda
 benefits from  more mature compilation and optimization  procedures.  Other less
 known environments  have been proposed, but  most of them have  been stopped, for

-example  we can  cite: FireStream  by ATI  which is  not maintened  anymore and
+example  we can  cite: FireStream  by ATI  which is  not maintained  anymore and

 replaced by  OpenCL, BrookGPU by  Standford University~\cite{ch1:Buck:2004:BGS}.
 Another environment based  on pragma (insertion of pragma  directives inside the
 code to  help the compiler to generate  efficient code) is call  OpenACC.  For a
 replaced by  OpenCL, BrookGPU by  Standford University~\cite{ch1:Buck:2004:BGS}.
 Another environment based  on pragma (insertion of pragma  directives inside the
 code to  help the compiler to generate  efficient code) is call  OpenACC.  For a


@@ -126,7 +126,7 @@ performance optimizations  such as speculative execution  which roughly speaking
 consists in executing  a small part of  code in advance even if  later this work
 reveals itself  to be  useless. On the  contrary, GPUs  do not have  low latency
 memory.   In comparison GPUs  have small  cache memories.  Nevertheless the
 consists in executing  a small part of  code in advance even if  later this work
 reveals itself  to be  useless. On the  contrary, GPUs  do not have  low latency
 memory.   In comparison GPUs  have small  cache memories.  Nevertheless the

-architecture of GPUs is optimized  for throughtput computation and it takes into
+architecture of GPUs is optimized  for throughput computation and it takes into

 account the memory latency.
 
 
 account the memory latency.
 
 


@@ -152,7 +152,7 @@ computation of other tasks.
 
 \section{Kinds of parallelism}
 
 
 \section{Kinds of parallelism}
 

-Many  kinds  of parallelism  are  avaible according  to  the  type of  hardware.
+Many  kinds  of parallelism  are  amiable according  to  the  type of  hardware.

 Roughly  speaking, there  are three  classes of  parallelism: instruction-level
 parallelism, data parallelism and task parallelism.
 
 Roughly  speaking, there  are three  classes of  parallelism: instruction-level
 parallelism, data parallelism and task parallelism.
 


@@ -170,7 +170,7 @@ data parallelism paradigm. This paradigm  is linked with the Single Instructions
 Multiple Data (SIMD)  architecture. This is the kind  of parallelism provided by
 GPUs.
 
 Multiple Data (SIMD)  architecture. This is the kind  of parallelism provided by
 GPUs.
 

-Taks parallelism is the common parallism  achieved out on clusters and grids and
+Task parallelism is the common parallelism  achieved out on clusters and grids and

 high performance  architectures where different tasks are  executed by different
 computing units.
 
 high performance  architectures where different tasks are  executed by different
 computing units.
 


@@ -187,7 +187,7 @@ architecture, the  performance is  led by the  use of  a huge number  of threads
 is no  context switching as in  CPUs and each  thread has its own  registers. In
 practice,  threads  are executed  by  SM  and are  gathered  into  groups of  32
 threads.  Those  groups  are  called  ``warps''. Each  SM  alternatively  executes
 is no  context switching as in  CPUs and each  thread has its own  registers. In
 practice,  threads  are executed  by  SM  and are  gathered  into  groups of  32
 threads.  Those  groups  are  called  ``warps''. Each  SM  alternatively  executes

-``active warps''  and warps becoming temporarilly  inactive due to  waiting of data
+``active warps''  and warps becoming temporarily  inactive due to  waiting of data

 (as shown in Figure~\ref{ch1:fig:latency_throughput}).
 
 The key to scalability in the CUDA model is the use of a huge number of threads.
 (as shown in Figure~\ref{ch1:fig:latency_throughput}).
 
 The key to scalability in the CUDA model is the use of a huge number of threads.