satisfied. \emph{Evolutionary algorithms}, \emph{swarm
optimization}, and \emph{ant colonies} fall into this class.
-
+%\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
solution-level\index{metaheuristics!solution-level parallelism}
parallel model is problem-dependent.}
\end{itemize}
-\clearpage
-\section{Challenges for the design of GPU-based metaheuristics}
+%\clearpage
+\section[Challenges for the design of GPU-based metaheuristics]{Challenges for the design of GPU-based\hfill\break metaheuristics}
\label{ch8:sec:challenges}
Developing GPU-based parallel
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
achieved by the GPU implementation compared
to the same sequential implementation on CPU using SA-matrix.
-\subsection[Implementing population-based metaheuristics\hfill\break on GPUs]{Implementing population-based metaheuristics on GPUs}
+\subsection[Implementing population-based metaheuristics 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
