X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/17bff40b83bcdcc39769f9e59c70ffae1c525b72..c08513a856650905e4751c8b52c7cbb661368a5f:/BookGPU/Chapters/chapter11/ch11.tex?ds=sidebyside

diff --git a/BookGPU/Chapters/chapter11/ch11.tex b/BookGPU/Chapters/chapter11/ch11.tex
index a374345..0aa6e8c 100644
--- a/BookGPU/Chapters/chapter11/ch11.tex
+++ b/BookGPU/Chapters/chapter11/ch11.tex
@@ -116,8 +116,8 @@ It is almost straightforward to parallelize this scheme for GPUs, by processing
 At the spline evaluation stage we need to compute $s(z_k)$ for a sequence of query values ${z_k}, k=1,\ldots,K$. For each $z_k$ we locate the interval $[t_i,t_{i+1}]$ containing $z_k$, using the bisection algorithm presented in Listing \ref{ch11:algeval}, and then apply the appropriate coefficients of the quadratic function. This is also  done in parallel.
 The bisection algorithm could be implemented using texture memory (to cache the array \texttt{z}), but this is not shown in Listing \ref{ch11:algeval}.
 
-%\pagebreak
-\lstinputlisting[label=ch11:algcoef,caption=Implementation of the kernel for calculating spline knots and coefficients; function fmax is used to avoid division by zero for data with coinciding abscissae.]{Chapters/chapter11/code1.cu}
+\pagebreak
+\lstinputlisting[label=ch11:algcoef,caption=implementation of the kernel for calculating spline knots and coefficients; function fmax is used to avoid division by zero for data with coinciding abscissae.]{Chapters/chapter11/code1.cu}
 
 
 %% \begin{figure}[!hp]
