%% %\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}}
\include{Chapters/symbollist}
\setcounter{page}{1}
+
\part{Presentation of GPUs}
\include{Chapters/chapter1/ch1}
\include{Chapters/chapter2/ch2}
\include{Chapters/chapter9/ch9}
\include{Chapters/chapter10/ch10}
+
+
\part{Numerical applications}
+
\include{Chapters/chapter7/ch7}
\include{Chapters/chapter11/ch11}
\include{Chapters/chapter12/ch12}
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.
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
\def\draw@part#1#2{%
\addpenalty{-\@highpenalty}%
- \vskip1em plus\p@
+ \vskip1.5em plus\p@
\@tempdima1.5em
\begingroup
\parindent\z@\rightskip\@pnumwidth
\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
\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
