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-\lstset{
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+%% \lstset{
+%% language=C,
+%% columns=fixed,
+%% basicstyle=\footnotesize\ttfamily,
+%% numbers=left,
+%% firstnumber=1,
+%% numberstyle=\tiny,
+%% stepnumber=5,
+%% numbersep=5pt,
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+%% frame=single,
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+%% % xleftmargin=17pt,
+%% % framexleftmargin=17pt,
+%% % framexrightmargin=5pt,
+%% % framexbottommargin=4pt,
+%% backgroundcolor=\color{lightgray},
+%% }
%\DeclareCaptionFont{blue}{\color{blue}}
%\captionsetup[lstlisting]{singlelinecheck=false, labelfont={blue}, textfont={blue}}
\subfigure[Image denoised by a $3\times 3$ median filter]{\label{img:sap_example_med3} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_med3.png}}\\
\subfigure[Image denoised by a $5\times 5$ median filter]{\label{img:sap_example_med5} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_med5.png}}\qquad
\subfigure[Image denoised by 2 iterations of a $3\times 3$ median filter]{\label{img:sap_example_med3_it2} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_med3_it2.png}}\\
- \caption{Exemple of median filtering, applied to salt \& pepper noise reduction.}
+ \caption{Example of median filtering, applied to salt \& pepper noise reduction.}
\label{fig:sap_examples}
\end{figure}
Since inner loops that fill the histogram vector contain very few fetching instructions (from 9 to 49, depending on the window size), it is not surprising to note their neglectable impact compared to outer loops that fetch image pixels (from 256k to 16M instructions).
One could be tempted to claim that CPU has no chance to win, which is not so obvious as it highly depends on what kind of algorithm is run and above all, how it is implemented. To illustrate this, we can notice that, despite a maximum effective throughput potential that is almost five times higher, measured GTX280 throughput values sometimes prove slower than CPU values, as shown in Table \ref{tab:medianHisto1}.
+
+\lstinputlisting[label={lst:medianGeneric},caption=Generic CUDA kernel achieving median filtering]{Chapters/chapter3/code/medianGeneric.cu}
+
+
On the GPU's side, we note high dependence on window size due to the redundancy induced by the multiple fetches of each pixel inside each block, becoming higher with the window size as illustrated by Figure \ref{fig:median_overlap}. On C2070 card, thanks to a more efficient caching mechanism, this effect is lesser. On GPUs, dependency over image size is low, and due to slightly more efficient data transfers when copying larger data amounts, pixel throughputs increases with image size. As an example, transferring a 4096$\times$4096 pixel image (32~MBytes) is a bit faster than transferring 64 times a 512$\times$512 pixel image (0.5~MBytes).
%% mettre l'eau à la bouche
-\lstinputlisting[label={lst:medianGeneric},caption=Generic CUDA kernel achieving median filtering]{Chapters/chapter3/code/medianGeneric.cu}
-
\begin{figure}
\centering
\includegraphics[width=8cm]{Chapters/chapter3/img/median_1.png}
- \caption{Exemple of 5x5 median filtering}
+ \caption{Example of 5x5 median filtering}
\label{fig:median_1}
\end{figure}
\begin{figure}
\centering
\includegraphics[width=6cm]{Chapters/chapter3/img/median5_overlap4.png}
- \caption{Reducing register count in a 5$\times$5 register-only median kernel outputting 2 pixels simultaneously. The first 7 forgetful selection stages are common to both processed center pixels. Only the last 5 selections have to be done separately.}
+ \caption[Reducing register count in a 5$\times$5 register-only median kernel outputting 2 pixels simultaneously.]{Reducing register count in a 5$\times$5 register-only median kernel outputting 2 pixels simultaneously. The first 7 forgetful selection stages are common to both processed center pixels. Only the last 5 selections have to be done separately.}
\label{fig:median5overlap}
\end{figure}
\begin{figure}
\centering
\includegraphics[width=6cm]{Chapters/chapter3/img/forgetful_selection4.png}
- \caption{First iteration of the $5\times 5$ selection process, with $k_{25}=14$, which shows how Instruction Level Parallelism is maximized by the use of an incomplete sorting network. Arrows represent the result of the swapping function, with the lowest value at the starting point and the highest value at the end point.}
+ \caption[First iteration of the $5\times 5$ selection process, with $k_{25}=14$, which shows how Instruction Level Parallelism is maximized by the use of an incomplete sorting network.]{First iteration of the $5\times 5$ selection process, with $k_{25}=14$, which shows how Instruction Level Parallelism is maximized by the use of an incomplete sorting network. Arrows represent the result of the swapping function, with the lowest value at the starting point and the highest value at the end point.}
\label{fig:median5overlap}
\end{figure}
\subfigure[Image denoised by a $3\times 3$ separable smoother]{\label{img:sap_example_sep_med3} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_sep_med3.png}}\\
\subfigure[Image denoised by a $5\times 5$ separable smoother]{\label{img:sap_example_sep_med5} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_sep_med5.png}}\qquad
\subfigure[Image background estimation by a $55\times 55$ separable smoother]{\label{img:sap_example_sep_med3_it2} \includegraphics[width=5cm]{Chapters/chapter3/img/airplane_sap25_sep_med111.png}}\\
- \caption{Exemple of separable median filtering (smoother), applied to salt \& pepper noise reduction.}
+ \caption{Example of separable median filtering (smoother), applied to salt \& pepper noise reduction.}
\label{fig:sap_examples2}
\end{figure}
