X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/d53d37855452b853b3db760e528dee96179dbe08..18cb85fd3967a4c46c9582e8515cef9af3448269:/BookGPU/Chapters/chapter1/ch1.tex

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index ee767b1..6c3f1f1 100755
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -1,7 +1,7 @@
 \chapterauthor{RaphaÃ«l Couturier}{Femto-ST Institute, University of Franche-Comte}
 
 
-\chapter{Presentation of the GPU architecture and of the CUDA environment}
+\chapter{Presentation of the GPU architecture and of the Cuda environment}
 \label{chapter1}
 
 \section{Introduction}\label{ch1:intro}
@@ -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
-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
-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
-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.
 
-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.
@@ -52,7 +52,7 @@ wrong, programmers had no way (and neither the tools) to detect it.
 
 \section{GPGPU}
 
-In order  to benefit from the computing  power of more recent  video cards, CUDA
+In order  to benefit from the computing  power of more recent  video cards, Cuda
 was first proposed in 2007 by  NVidia. It unifies the programming model for some
 of  their most performant  video cards.   Cuda~\cite{ch1:cuda} has  quickly been
 considered by  the scientific community as  a great advance  for general purpose
@@ -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
-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
-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
@@ -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
-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.
 
 
@@ -143,7 +143,7 @@ by one with a short memory latency to get the data to process. After some tasks,
 there is  a context switch  that allows the  CPU to run  concurrent applications
 and/or multi-threaded  applications. Memory latencies  are longer in a  GPU, the
 the  principle  to   obtain  a  high  throughput  is  to   have  many  tasks  to
-compute. Later we  will see that those tasks are called  threads with CUDA. With
+compute. Later we  will see that those tasks are called  threads with Cuda. With
 this  principle, as soon  as a  task is  finished the  next one  is ready  to be
 executed  while the  wait for  data for  the previous  task is  overlapped by
 computation of other tasks.
@@ -152,7 +152,7 @@ computation of other tasks.
 
 \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.
 
@@ -170,38 +170,38 @@ data parallelism paradigm. This paradigm  is linked with the Single Instructions
 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.
 
-\section{CUDA Multithreading}
+\section{Cuda Multithreading}
 
-The data parallelism  of CUDA is more precisely based  on the Single Instruction
+The data parallelism  of Cuda is more precisely based  on the Single Instruction
 Multiple Thread (SIMT) model. This is due to the fact that a programmer accesses
-to  the cores  by the  intermediate of  threads. In  the CUDA  model,  all cores
+to  the cores  by the  intermediate of  threads. In  the Cuda  model,  all cores
 execute the  same set of  instructions but with  different data. This  model has
 similarities with the vector programming  model proposed for vector machines through
-the  1970s into  the  90s, notably  the  various Cray  platforms.   On the  CUDA
+the  1970s into  the  90s, notably  the  various Cray  platforms.   On the  Cuda
 architecture, the  performance is  led by the  use of  a huge number  of threads
 (from thousands up to  to millions). The particularity of the  model is that there
 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.
+The key to scalability in the Cuda model is the use of a huge number of threads.
 In practice, threads are not only gathered  in warps but also in thread blocks. A
 thread block is executed  by only one SM and it cannot  migrate. The typical size of
 a thread block is a number power of two (for example: 64, 128, 256 or 512).
 
 
 
-In this  case, without changing anything inside  a CUDA code, it  is possible to
-run your  code with  a small CUDA  device or  the most performing Tesla  CUDA cards.
+In this  case, without changing anything inside  a Cuda code, it  is possible to
+run your  code with  a small Cuda  device or  the most performing Tesla  Cuda cards.
 Blocks are  executed in any order depending  on the number of  SMs available. So
 the  programmer  must  conceive  its  code  having this  issue  in  mind.   This
-independence between thread blocks provides the scalability of CUDA codes.
+independence between thread blocks provides the scalability of Cuda codes.
 
 \begin{figure}[b!]
 \centerline{\includegraphics[scale=0.65]{Chapters/chapter1/figures/scalability.pdf}}
@@ -217,7 +217,7 @@ practice, the number of  thread blocks and the size of thread  blocks is given a
 parameters  to  each  kernel.   Figure~\ref{ch1:fig:scalability}  illustrates  an
 example of a kernel composed of 8 thread blocks. Then this kernel is executed on
 a small device containing only 2 SMs.  So in  this case, blocks are executed 2
-by 2 in any order.  If the kernel is executed on a larger CUDA device containing
+by 2 in any order.  If the kernel is executed on a larger Cuda device containing
 4 SMs, blocks are executed 4 by 4 simultaneously.  The execution times should be
 approximately twice faster in the latter  case. Of course, that depends on other
 parameters that will be described later.
@@ -291,13 +291,6 @@ illustrated focusing on the particularity of GPUs in term of parallelism, memory
 latency and  threads. In order to design  an efficient algorithm for  GPU, it is
 essential to have all these parameters in mind.
 
-%%http://people.maths.ox.ac.uk/gilesm/pp10/lec2_2x2.pdf
-%%https://people.maths.ox.ac.uk/erban/papers/paperCUDA.pdf
-%%http://forum.wttsnxt.com/my_forum/viewtopic.php?f=5&t=9519
-%%http://www.cs.nyu.edu/manycores/cuda_many_cores.pdf
-%%http://www.cc.gatech.edu/~vetter/keeneland/tutorial-2011-04-14/02-cuda-overview.pdf
-%%http://people.maths.ox.ac.uk/~gilesm/cuda/
-
 
 \putbib[Chapters/chapter1/biblio]
 
