correct

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

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index b15918e453dd568c278db035ea6bdb08fe78f5ba..6c3f1f1e6d67448a79c761f2d84bbe7905011f81 100755 (executable)
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -1,7 +1,7 @@
 \chapterauthor{Raphaël Couturier}{Femto-ST Institute, University of Franche-Comte}
 
 
 \chapterauthor{Raphaël 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}
 \label{chapter1}
 
 \section{Introduction}\label{ch1:intro}


@@ -12,9 +12,9 @@ execution of some algorithms. First of all this chapter gives a brief history of
 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
 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
+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
 programmer needs to use threads. They have some particularities which enable the

-CUDA model to be efficient and scalable when some constraints are addressed.
+Cuda model to be efficient and scalable when some constraints are addressed.

 
 
 
 
 
 


@@ -52,7 +52,7 @@ wrong, programmers had no way (and neither the tools) to detect it.
 
 \section{GPGPU}
 
 
 \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
 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


@@ -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
 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.
 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.


@@ -174,14 +174,14 @@ Task parallelism is the common parallelism  achieved out on clusters and grids a
 high performance  architectures where different tasks are  executed by different
 computing units.
 
 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
 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
 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
 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


@@ -190,18 +190,18 @@ threads.  Those  groups  are  called  ``warps''. Each  SM  alternatively  execut
 ``active warps''  and warps becoming temporarily  inactive due to  waiting of data
 (as shown in Figure~\ref{ch1:fig:latency_throughput}).
 
 ``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 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
 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}}
 
 \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
 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.
 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.
 
 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]
 
 
 \putbib[Chapters/chapter1/biblio]