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[book_gpu.git] / BookGPU / Chapters / chapter9 / ch9.tex

diff --git a/BookGPU/Chapters/chapter9/ch9.tex b/BookGPU/Chapters/chapter9/ch9.tex
index 0294f4887303bb76aaaf4bf6c14cc246b0e2639c..442c53a03ba02139a8b7ac7f8c68b084c9723823 100644 (file)
--- a/BookGPU/Chapters/chapter9/ch9.tex
+++ b/BookGPU/Chapters/chapter9/ch9.tex
@@ -99,7 +99,7 @@ solutions. The process is repeated until a stopping criterion is
 satisfied. \emph{Evolutionary algorithms}, \emph{swarm
 optimization}, and \emph{ant colonies} fall into this class.
 
-\clearpage
+%\clearpage
 \section{Parallel models for metaheuristics}\label{ch8:sec:paraMeta}
 Optimization problems, whether real-life or academic, are more
 often NP-hard and CPU time and/or memory consuming. Metaheuristics
@@ -187,7 +187,7 @@ consuming. Unlike the two previous parallel models, the
 solution-level\index{metaheuristics!solution-level parallelism}
 parallel model is problem-dependent.}
 \end{itemize}
-\clearpage
+%\clearpage
 \section[Challenges for the design of GPU-based  metaheuristics]{Challenges for the design of GPU-based\hfill\break  metaheuristics}
 \label{ch8:sec:challenges}
 
@@ -251,7 +251,7 @@ data (e.g., data for fitness evaluation that all threads
 concurrently access) on the constant memory, and the most accessed
 data structures (e.g., population of individuals for a CUDA thread
 block) on the shared memory.
-
+\clearpage
 \subsection{Threads synchronization}
 \index{GPU!threads synchronization} The thread
 synchronization issue is caused by both the GPU architecture and