X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/17d1891ee5feec4f52ef9c51bfa60b78f0bd14c2..0f26b548f3029a96eafd6667024aabe2b36e464b:/BookGPU/Chapters/chapter4/ch4.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter4/ch4.tex b/BookGPU/Chapters/chapter4/ch4.tex
index 0a0d6cb..90612c9 100644
--- a/BookGPU/Chapters/chapter4/ch4.tex
+++ b/BookGPU/Chapters/chapter4/ch4.tex
@@ -8,7 +8,7 @@
 
 \section{Overview}
 In this chapter, after dealing with GPU median filter implementations,
-we propose to explore how convolutions\index{Convolution}  can be implemented on modern
+we propose to explore how convolutions\index{convolution}  can be implemented on modern
 GPUs. Widely used in digital image processing filters, the \emph{convolution
 operation} basically consists of taking the sum of products of elements
 from two 2D functions, letting one of the two functions move over
@@ -20,7 +20,7 @@ to $I$ as an $H\times L$ pixel gray-level image and to $I(x,y)$ as the gray-leve
 value of each pixel of coordinates $(x,y)$.
 
 
-
+\clearpage
 \section{Definition}
 Within a digital image $I$, the convolution operation is performed between
 image $I$ and convolution mask \emph{h} (To avoid confusion with other
@@ -81,7 +81,7 @@ This first implementation consists of a rather naive application to
 convolutions of the techniques applied to median filters in the
 previous chapter, as a reminder: texture memory used with incoming
 data, pinned memory with output data, optimized use of registers
-while processing data and multiple output per thread\index{Multiple output per thread}. 
+while processing data and multiple output per thread\index{multiple output per thread}. 
 One significant difference lies in the fact
 that the median filter uses only one parameter, the size of the window mask,
 which can be hard-coded, while a convolution mask requires referring to several parameters; hard-coding
@@ -239,8 +239,8 @@ However, our technique requires writing one kernel per mask size, which can be s
 
 \lstinputlisting[label={lst:convoGene8x8pL3},caption=CUDA kernel achieving a $3\times 3$ convolution operation with the mask in symbol memory and direct data fetches in texture memory]{Chapters/chapter4/code/convoGene8x8pL3.cu}
 
-\subsection{Using shared memory to store prefetched data\index{Prefetching}.}
- \index{memory~hierarchy!shared~memory}
+\subsection{Using shared memory to store prefetched data\index{prefetching}.}
+ \index{memory hierarchy!shared memory}
 A more convenient way of coding a convolution kernel is to use shared memory to perform a prefetching stage of the whole halo before computing the convolution sums.
 This proves to be quite efficient and more versatile, but it obviously generates some overhead because 
 \begin{itemize}
@@ -356,7 +356,7 @@ $\mathbf{4096\times 4096}$&1.533 \\\hline
 \label{tab:cpyToArray}
 \end{table}
 \lstinputlisting[label={lst:convoSepSh},caption=data copy between the calls to 1D convolution kernels achieving a 2D separable convolution operation]{Chapters/chapter4/code/convoSepSh.cu}
-\lstinputlisting[label={lst:convoSepShV},caption=CUDA kernel achieving a horizontal 1D convolution operation after a preloading \index{Prefetching} of data into shared memory]{Chapters/chapter4/code/convoSepShV.cu}
+\lstinputlisting[label={lst:convoSepShV},caption=CUDA kernel achieving a horizontal 1D convolution operation after a preloading \index{prefetching} of data into shared memory]{Chapters/chapter4/code/convoSepShV.cu}
 \lstinputlisting[label={lst:convoSepShH},caption=CUDA kernel achieving a vertical 1D convolution operation after a preloading of data into shared memory]{Chapters/chapter4/code/convoSepShH.cu}
  
 \section{Conclusion}