ch1

[book_gpu.git] / BookGPU / Chapters / chapter17 / ch17.tex

diff --git a/BookGPU/Chapters/chapter17/ch17.tex b/BookGPU/Chapters/chapter17/ch17.tex
index 9b1c611f814b9dd1d3f667842ad18d687d6e7ab5..d6e3a314b80676b8f715167c02acc01b2976cbcc 100755 (executable)
--- a/BookGPU/Chapters/chapter17/ch17.tex
+++ b/BookGPU/Chapters/chapter17/ch17.tex
@@ -1,8 +1,8 @@
-\chapterauthor{Guillaume Laville}{Femto-ST Institute, University of Franche-Comte, France}
-\chapterauthor{Christophe Lang}{Femto-ST Institute, University of Franche-Comte, France}
-\chapterauthor{Kamel Mazouzi}{Femto-ST Institute, University of Franche-Comte, France}
-\chapterauthor{Nicolas Marilleau}{Femto-ST Institute, University of Franche-Comte, France}
-\chapterauthor{Bénédicte Herrmann}{Femto-ST Institute, University of Franche-Comte, France}
+\chapterauthor{Guillaume Laville, Christophe Lang, Bénédicte Herrmann and Laurent Philippe}{Femto-ST Institute, University of Franche-Comte, France}
+%\chapterauthor{Christophe Lang}{Femto-ST Institute, University of Franche-Comte, France}
+\chapterauthor{Kamel Mazouzi}{Franche-Comte Computing Center, University of Franche-Comte, France}
+\chapterauthor{Nicolas Marilleau}{UMMISCO, Institut de Recherche pour le Developpement (IRD), France}
+%\chapterauthor{Bénédicte Herrmann}{Femto-ST Institute, University of Franche-Comte, France}

 %\chapterauthor{Laurent Philippe}{Femto-ST Institute, University of Franche-Comte, France}
 
 \newlength\mylen
 %\chapterauthor{Laurent Philippe}{Femto-ST Institute, University of Franche-Comte, France}
 
 \newlength\mylen


@@ -11,7 +11,7 @@
   \setlength\hangindent{\mylen}%
   \hspace*{\mylen}#1\\}
 
   \setlength\hangindent{\mylen}%
   \hspace*{\mylen}#1\\}
 

-\chapter{Implementing MAS on GPU}
+\chapter{Implementing Multi-Agent Systems on GPU}

 \label{chapter17}
 
 
 \label{chapter17}
 
 


@@ -47,7 +47,7 @@ solution to increase simulation performance but Graphical Processing
 Units (GPU) are also a promising technology with an attractive
 performance/cost ratio.
 
 Units (GPU) are also a promising technology with an attractive
 performance/cost ratio.
 

-Conceptually a MAS is a distributed system as it favors the definition
+Conceptually a MAS\index{Multi-Agent System} is a distributed system as it favors the definition

 and description of large sets of individuals, the agents, that can be
 run in parallel. As a large set of agents could have the same behavior
 a SIMD model should fit the simulation execution. Most of the
 and description of large sets of individuals, the agents, that can be
 run in parallel. As a large set of agents could have the same behavior
 a SIMD model should fit the simulation execution. Most of the


@@ -299,7 +299,7 @@ collembolas in fields and forests. It is based on a diffusion
 algorithm which illustrates the case of agents with a simple behavior
 and few synchronization problems.
 
 algorithm which illustrates the case of agents with a simple behavior
 and few synchronization problems.
 

-\subsection{The Collembola model}
+\subsection{The Collembola model\index{Collembola model}}

 \label{ch17:subsec:collembolamodel}
 
 The Collembola model is an example of multi-agent system using GIS (Geographical Information System)
 \label{ch17:subsec:collembolamodel}
 
 The Collembola model is an example of multi-agent system using GIS (Geographical Information System)


@@ -374,6 +374,7 @@ the total numbers of access needed to updated those populations.
 
 %\lstinputlisting[language=C,caption=Collembola OpenCL
 %kernels,label=fig:collem_kernels]{Chapters/chapter17/code/collem_kernels.cl}
 
 %\lstinputlisting[language=C,caption=Collembola OpenCL
 %kernels,label=fig:collem_kernels]{Chapters/chapter17/code/collem_kernels.cl}

+\pagebreak

 \lstinputlisting[caption=Collembola OpenCL Diffusion kernel,label=ch17:listing:collembola-diffuse]{Chapters/chapter17/code/collem_kernel_diffuse.cl}
 
 The reproduction, diffusion and culling steps are implemented on GPU
 \lstinputlisting[caption=Collembola OpenCL Diffusion kernel,label=ch17:listing:collembola-diffuse]{Chapters/chapter17/code/collem_kernel_diffuse.cl}
 
 The reproduction, diffusion and culling steps are implemented on GPU


@@ -646,6 +647,7 @@ implementation by enforcing a quasi-sequential execution. It is
 necessary, in the case of MIOR as well as for other ABM, to ensure
 that each work-item is not too constrained in its execution.
 
 necessary, in the case of MIOR as well as for other ABM, to ensure
 that each work-item is not too constrained in its execution.
 

+\pagebreak

 \lstinputlisting[caption=Main MIOR kernel,label=ch17:listing:mior_kernels]{Chapters/chapter17/code/mior_kernels.cl}
 
 From the sequential algorithm 1 where all the agents share the same
 \lstinputlisting[caption=Main MIOR kernel,label=ch17:listing:mior_kernels]{Chapters/chapter17/code/mior_kernels.cl}
 
 From the sequential algorithm 1 where all the agents share the same