From ca38d6218b9ba6a0fd2e0b2126c6b38146504b8e Mon Sep 17 00:00:00 2001
From: couturie <couturie@extinction>
Date: Thu, 29 Aug 2013 19:53:21 +0200
Subject: [PATCH] new

---
 BookGPU/BookGPU.tex               | 6 +++++-
 BookGPU/Chapters/chapter3/ch3.tex | 2 +-
 BookGPU/Chapters/chapter9/ch9.tex | 2 +-
 BookGPU/sunil.cls                 | 6 +++---
 4 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/BookGPU/BookGPU.tex b/BookGPU/BookGPU.tex
index 16c1f8f..0a5f5c8 100755
--- a/BookGPU/BookGPU.tex
+++ b/BookGPU/BookGPU.tex
@@ -147,7 +147,7 @@
 
 %%   %\captionsetup[lstlisting]{singlelinecheck=false, labelfont={blue}, textfont={blue}}
 \DeclareCaptionFont{white}{\color{white}}
-\DeclareCaptionFormat{listing}{\colorbox[cmyk]{0.43, 0.35, 0.35,0.01}{\parbox{\textwidth}{\hspace{15pt}#1#2#3}}}
+\DeclareCaptionFormat{listing}{\hspace{-3.5pt}\colorbox[cmyk]{0.43, 0.35, 0.35,0.01}{\parbox{1.003\textwidth}{\hspace{15pt}#1#2#3}}}
 \captionsetup[lstlisting]{format=listing,labelfont=white,textfont=white, singlelinecheck=false, margin=0pt, font={bf,footnotesize}}
 
 
@@ -188,6 +188,7 @@
 \include{Chapters/symbollist}
 
 \setcounter{page}{1}
+
 \part{Presentation of GPUs}
 \include{Chapters/chapter1/ch1}
 \include{Chapters/chapter2/ch2}
@@ -202,7 +203,10 @@
 \include{Chapters/chapter9/ch9}
 \include{Chapters/chapter10/ch10}  
 
+
+
 \part{Numerical applications}
+
 \include{Chapters/chapter7/ch7} 
 \include{Chapters/chapter11/ch11}
 \include{Chapters/chapter12/ch12}
diff --git a/BookGPU/Chapters/chapter3/ch3.tex b/BookGPU/Chapters/chapter3/ch3.tex
index 1cf40c7..1c6453d 100755
--- a/BookGPU/Chapters/chapter3/ch3.tex
+++ b/BookGPU/Chapters/chapter3/ch3.tex
@@ -93,7 +93,7 @@ Median filtering is a well-known method used in a wide range of application fram
 First introduced by Tukey in \cite{tukey77}, it has been widely studied since then, and many researchers have proposed efficient implementations of it, adapted to various hypotheses, architectures and processors. 
 Originally, its main drawbacks were its compute complexity, its nonlinearity and its data-dependent runtime. Several researchers have addressed these issues and designed, for example, efficient histogram-based median filters with predictible runtimes \cite{Huang:1981:TDS:539567, Weiss:2006:FMB:1179352.1141918}.  
 
-More recently, the advent of GPUs opened new perspectives in terms of image processing performance, and some researchers managed to take advantage of the new graphics capabilities: in that respect, we can cite the Branchless Vectorized Median (BVM) filter \cite{5402362, chen09} which allows very interesting runtimes on CUDA-enabled devices but, as far as we know, the fastest implementation to date is the histogram-based PCMF median filter \cite{Sanchez-2-2012}.
+More recently, the advent of GPUs opened new perspectives in terms of image processing performance, and some researchers managed to take advantage of the new graphics capabilities: in that respect, we can cite the Branchless Vectorized Median (BVM) filter \cite{5402362, chen09} which allows very interesting runtimes on CUDA-enabled devices but, as far as we know, the fastest implementation to date is the histogram-based Parallel Ccdf-based Median Filter (PCMF) \cite{Sanchez-2-2012} where Ccdf means Complementary Cumulative Distribution Function.
 
 Some of the following implementations feature very fast runtimes. They are targeted on NVIDIA Tesla GPU (Fermi architecture, compute capability 2.x) but may easily be adapted to other models, e.g., those of compute capability 1.3.
 
diff --git a/BookGPU/Chapters/chapter9/ch9.tex b/BookGPU/Chapters/chapter9/ch9.tex
index c782d5b..4ad0d6f 100644
--- a/BookGPU/Chapters/chapter9/ch9.tex
+++ b/BookGPU/Chapters/chapter9/ch9.tex
@@ -501,7 +501,7 @@ QAPLIB~\cite{burkard1991qaplib}. Speedups up to $10 \times$ are
 achieved by the GPU implementation compared
 to the same sequential implementation on CPU using SA-matrix.\\
 
-\subsection{Implementing population-based metaheuristics on GPUs}
+\subsection[Implementing population-based metaheuristics\hfill\break on GPUs]{Implementing population-based metaheuristics on GPUs}
 
 State-of-the-art works dealing with the implementation of
 p-metaheuristics on GPUs generally rely on parallel models and
diff --git a/BookGPU/sunil.cls b/BookGPU/sunil.cls
index 1455f91..2be566f 100755
--- a/BookGPU/sunil.cls
+++ b/BookGPU/sunil.cls
@@ -1029,7 +1029,7 @@
 
 \def\draw@part#1#2{%
   \addpenalty{-\@highpenalty}%
-  \vskip1em plus\p@
+  \vskip1.5em plus\p@
   \@tempdima1.5em
   \begingroup
     \parindent\z@\rightskip\@pnumwidth
@@ -1082,7 +1082,7 @@
 \def\@pnumwidth{1.8em}
 \def\draw@chapter#1#2{%
   \addpenalty{-\@highpenalty}%
-  \vskip1em plus\p@
+  \vskip1.5em plus\p@
   \@tempdima1.5em
   \begingroup
     \parindent\z@\rightskip\@pnumwidth
@@ -1115,7 +1115,7 @@
 \def\draw@authors{%
   \let\@t\@authors
         \hskip\leftskip
-  \noindent\vbox{\hsize26pc\raggedright\addvspace{4pt}\ifx\@t\@empty
+  \noindent\vbox{\hsize25pc\raggedright\addvspace{4pt}\ifx\@t\@empty
     \let\@t\last@author\fi
   \ifx\@t\@empty\else
     \hskip\leftskip
-- 
2.39.5