new

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

\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
\newcommand\myinput[1]{%
  \settowidth\mylen{\KwIn{}}%
  \setlength\hangindent{\mylen}%
  \hspace*{\mylen}#1\\}

\chapter{Implementing Multi-Agent Systems on GPU}
\label{chapter17}


\section{Introduction}
\label{ch17:intro}

In this chapter we introduce the use of Graphical Processing Units
(GPU) for multi-agents-based systems as an example of a not-so-regular
application that could benefit from the GPU computing
power. Multi-Agent Systems (MAS) are a simulation paradigm used to
study the behavior of dynamic systems. Dynamic systems as physical
systems are often modeled by mathematical representations and their
dynamic behavior is simulated by differential equations. The
simulation of the system thus often relies on the resolution of a
linear system that can be efficiently computed on a graphical
processing unit as shown in the preceding chapters. But when the
behavior of the system elements is not uniformly driven by the same
law, when these elements have their own behavior, the modeling process
is too complex to rely on formal expressions. In this context MAS is a
recognized approach to model and simulate systems where individuals
have an autonomous behavior that cannot be simulated by the evolution
of a set of variables driven by mathematical laws. MAS are often used
to simulate natural or collective phenomena whose individuals are too
numerous or various to provide a unified algorithm describing the
system evolution. The agent-based approach is to divide these complex
systems into individual self-contained entities with their smaller set
of attributes and functions. But, as for mathematical simulations,
when the size of the MAS increases, the need of computing power and
memory also increases. For this reason, multi-agent systems should
benefit from the use of distributed computing architectures. Clusters
and grids are often identified as the main solution to increase
simulation performance but GPUs are also a promising technology with
an attractive performance/cost ratio.

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 Single Instruction Multiple
Data (SIMD) execution architecture should fit the simulation
execution. Most of the agent-based simulators are, however, designed
with a sequential scheme in mind, and these simulators seldom use more
than one core for their execution. Due to simulation scheduling
constraints, data sharing and exchange between agents and the huge
amount of interactions between agents and their environment, it is
indeed rather difficult to distribute an agent based simulator, for
instance, to take advantage of new multithreaded computer
architectures. Thus, guidelines and tools dedicated to MAS paradigm
and High Performance Computing (HPC) are now a need for other complex
system communities. Note that, from the described structure (large
number of agents sharing data), we can conclude that MAS would more
easily benefit from many-core architectures than from other kinds of
parallelism.

Another key point that advocates for the use of many-core in MAS is
the growing need for multiscale simulations. Multiscale simulations
explore problems with interactions between several scales. The
different scales use different granularity of the structure and
potentially different models. Most of the time the lower scale
simulations provide results to higher scale simulations. In that case
the execution of the simulations can easily be distributed between the
local cores and a many-core architecture, i.e., a GPU device.

We explore in this chapter the use of many-core architectures to
execute agent-based simulations. We illustrate our reflexion with two
cases: the Collembola simulator designed to simulate the diffusion of
Collembola between plots of land and the MIOR (MIcro ORganism)
simulator that reproduces effects of earthworms on bacteria dynamics
in a bulked soil. In Section \ref{ch17:ABM} we present the work
related to MAS and parallelization with a special focus on many-core
use. In sections \ref{ch17:sec:1stmodel} and \ref{ch17:sec:2ndmodel}
we present in detail two multi-agent models, their GPU
implementations, the conducted experiments, and their performance
results. The first model, given in Section \ref{ch17:sec:1stmodel},
illustrates the use of a GPU architecture to speed up the execution of
some computation-intensive functions while the main model is still
executed on the central processing unit. The second model, given in
Section \ref{ch17:sec:2ndmodel}, illustrates the use of a GPU
architecture to implement the whole model on the GPU processor which
implies deeper changes in the initial algorithm. In Section
\ref{ch17:analysis} we propose a more general reflexion on these
implementations and provide some guidelines. Then, we conclude in
Section \ref{ch17:conclusion} on the possible generalization of our
work.


\section{Running agent-based simulations}
\label{ch17:ABM}

In this section, we present the context of MAS, their parallelization,
and we report several existing works on using GPU to simulate
multi-agent systems.

\subsection{Multi-agent systems and parallelism}

Agent-based systems are often used to simulate natural or collective
phenomena whose actors are too numerous or various to provide a simple
unified algorithm describing the studied system
dynamic~\cite{Schweitzer2003}. The implementation of an agent based
simulation usually starts by designing the underlying agent-based
model (ABM). Most ABM are based around a few types of entities such as
agents, one environment, or an interaction
organization~\cite{Vowel02}. In the complex system domain, the
environment often describes a real space, its structure (e.g. soil
textures and porosities), and its dynamics (e.g., organic matter
decomposition). It is a virtual world in which agents represent
studied entities (e.g., biotic organisms) evolution. The actual agent
is animated by a behavior that can range between reactivity (only
reacts to external stimuli) and cognition (makes complex decisions
based on environmental and internal factors). Interaction and
organization define functions, types, and patterns of communications
of their member agents in the
system~\cite{Odell:2003:RRD:1807559.1807562}. Note that, depending on
the MAS, agents can communicate either directly through special
primitives or indirectly through the information stored in the
environment.

Agent-based simulations have been used for more than a decade to
reproduce, understand and even predict complex system dynamics. They
have proved their usefulness in various scientific
communities. Nowadays generic agent based frameworks such as
Repast~\cite{repast_home} or NetLogo~\cite{netlogo_home} are promoted
to implement simulators. Many ABMs such as the crown model
representing a city wide scale~\cite{Strippgen_Nagel_2009} tend
however to require a large number of agents to provide a realistic
behavior and reliable global statistics. Moreover, an achieved model
analysis needs to resort to an experiment plan, consisting of multiple
simulation runs, to obtain enough confidence in a simulation. In this
case the available computing power often limits the simulation size,
and the resulting range thus requires the use of parallelism to
explore bigger configurations.

For that, three major approaches can be identified:
\begin{enumerate}
\item parallelizing experiments execution on a cluster or a grid (one
  or a few simulations are submitted to each
  core)~\cite{Blanchart11,Chuffart2010},
\item parallelizing the simulator on a cluster (the environment of the
  MAS is split and run on several distributed
  nodes)~\cite{Cosenza2011,MAR06},
\item optimizing the simulator by taking advantage of computer
  resources (multi-threading, GPU, and so on) \cite{Aaby10}.
\end{enumerate}

In the first case, experiments are run independent of each other and
only simulation parameters are changed between two runs so that a
simple version of an existing simulator can be used. This approach
does not, however, allow to run larger models.  In the second and the
third case, model and code modifications are necessary. Only a few
frameworks, however, introduce distribution in agent simulation
(Madkit~\cite{Gutknecht2000}, MASON~\cite{Sean05},
repastHPC~\cite{Collier11}), and parallel implementations are often
based on the explicit use of threads using shared
memory~\cite{Guy09clearpath} or cluster libraries such as
MPI~\cite{Kiran2010}.

Parallelizing a multi-agent simulation is, however, complex due to space
and time constraints. Multi-agent simulations are usually based on a
synchronous execution: at each time step, numerous events (space data
modification, agent motion) and interactions between agents happen.
Distributing the simulation on several computers or grid nodes to guarantee a distributed synchronous execution and
coherency. This often leads to poor performance or complex
synchronization problems. Multicore execution or delegating part of
this execution to others processors such as GPUs~\cite{Bleiweiss_2008}
is usually easier to implement since all the threads share the data
and the local clock.

% Different agent patterns can be adopted in an ABMs such as
% cognitive and reactive ones~\cite{Ferber99}. Cognitive agents act on
% the environment and interact with other agents according to a complex
% behavior. This behavior takes a local perception of the virtual world
% and the agent past (a memory characterized by an internal state and
% belief, imperfect knowledge about the world) into account. Reactive
% agents have a much systematic pattern of action based on stimuli
% response schemes (no or few knowledge and state conservation in agent). The
% evolution of the ABM environment, in particular, is often represented with
% this last kind of agents. As their behavior is usually simple, we
% propose in this chapter to delegate part of the environment and of the
% reactive agents execution to the graphical processing unit of the
% computer. This way we can balance the load between both CPU and GPU
% execution resources.

% In the particular case of multi-scale simulations such as the Sworm
% simulation~\cite{BLA09} the environment may be used at different
% levels. Since the representation of the whole simulated environment
% (i.e. the soil area) would be costly, the environment is organized as
% a multi-level tree of small soil cubes which can be lazily
% instantiated during the simulation. This allows to gradually refine
% distribution details in units of soil as agents progress and need
% those information, by using a fractal process based on the
% bigger-grained already instantiated levels. This characteristic,
% especially for a fractal model, could be the key of the
% distribution. For instance, each branch of a fractal environment could
% be identified as an independent area and parallelized. In addition
% Fractal is a famous approach to describe multi-scale environment (such
% as soil) and its organization~\cite{perrier}. In that case the lower
% scale simulations can also be delegated to the GPU card to limit the
% load of the main (upper scale) simulation.

\subsection{MAS implementation on GPU}
\label{ch17:subsec:gpu}

The last few years have seen the appearance of new generations of
graphic cards based on more general purpose execution units which are
promising for large systems such as MAS. Using matrix-based data
representations and SIMD computations is however not always
straightforward in MAS, where data structures and algorithms are
tightly coupled to the described simulation. However, works from
existing literature show that MAS can benefit from these performance
gains on various simulation types, such as traffic
simulation~\cite{Strippgen_Nagel_2009}, cellular
automata~\cite{Dsouza2007}, mobile-agent based
path-finding~\cite{Silveira:2010:PRG:1948395.1948446} or genetic
algorithms~\cite{Maitre2009}. Note that an application-specific
adaptation process was required in the case of these MAS: some of the
previous examples are driven by mathematical laws (path-finding) or
use a natural mapping between a discrete environment (cellular
automaton) and GPU cores. Unfortunately, this mapping often requires
algorithmic adaptations in other models but experience shows that the
more reactive a MAS is the more adapted its implementation is to GPU.

The first step in the adaptation of an ABM to GPU platforms is the
choice of language. On the one hand, the Java programming language is
often used for the implementation of MAS due to its availability on
numerous platforms or frameworks and its focus on high-level,
object-oriented programming. On the other hand, GPU platforms can only
run specific languages such as OpenCL or CUDA. OpenCL (supported on
AMD, Intel, and NVIDIA hardware) better suits the portability concerns
across a wide range of hardware needed the agent simulators, as
opposed to CUDA which is an NVIDIA-specific library.

OpenCL is a C library which provides access to the underlying CPU or
GPU threads using an asynchronous interface. Various OpenCL functions
allow the compilation and the execution of programs on these execution
resources, the copying of data buffers between devices, and the
collection of profiling information.

This library is based around three main concepts:

\begin{itemize}
\item the \emph{kernel} (similar to a CUDA kernel), which represents a runnable program
  containing instructions to be executed on the GPU;
\item the \emph{work-item} (equivalent to a CUDA thread), which is analogous to the concept
  of thread on GPU, in that it represents one running instance of a
  GPU kernel; and
\item the \emph{work-group} (or execution block) which is a set of work-items
  sharing some memory to speed up data accesses and
  computations. Synchronization operations such as barrier can only be
  used across the same work-group.
\end{itemize}

Running an OpenCL computation consists of launching numerous
work-items that execute the same kernel. The work-items are submitted
to a submission queue to optimize the available cores usage. A
calculus is achieved once all these kernel instances have terminated.

The number of work-items used in each work-group is an important
implementation choice which determines how many tasks will share the
same cache memory. Data used by the work-items can be stored as
N-dimensions matrices in local or global GPU memory.  Since the size
of this memory is often limited to a few hundred kilobytes, choosing
this number often implies a compromise between the model
synchronization or data requirements and the available resources.

In the case of agent-based simulations, each agent can be naturally
mapped to a work-item. Work-groups can then be used to represent
groups of agents or simulations sharing common data (such as the
environment) or algorithms (such as the background evolution process).

In the following examples a binding named JOCL~\cite{jocl_home} is
used to access the OpenCL platform from the Java programming language.

In the next sections we present two practical cases that will be
studied in detail, from the model to its implementation and
performance.

\section{A first practical example}
\label{ch17:sec:1stmodel}

The first model, the Collembola model, simulates the propagation of
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.

\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) and survey data (population count)
to model the evolution of the biodiversity across land plots. A first
version of this model has been developed with the Netlogo framework by
Bioemco and UMMISCO researchers. In this model, the biodiversity is
modeled by populations of athropod individuals, the Collembola, which
can reproduce and diffuse to favorable new habitats. The simulator
allows us to study the diffusion of Collembola, between plots of land
depending on their use (artifical soil, crop, forest, etc.) In this
model the environment is composed of the studied land, and collembola
are used as agents. Every land plot is divided into several cells,
each cell representing a surface unit (16x16 meters). A number of
individuals per collembola species is associated to each cell. The
model evolution is then based on a common diffusion model that
diffuses individuals between cells. Each step of the simulation is
based on four stages, as shown on
Figure~\ref{ch17:fig:collem_algorithm}:

% \begin{enumerate}
% \item arrival of new individuals
% \item reproduction in each cell
% \item diffusion between cells
% \item updating of colembola lifetime
% \end{enumerate}

\begin{figure}[h]
\centering
\includegraphics[width=0.6\textwidth]{Chapters/chapter17/figs/algo_collem.pdf}
\caption{Evolution algorithm of the Collembola model.}
\label{ch17:fig:collem_algorithm}
\end{figure}

The algorithm is quite simple but includes two costly operations, the
reproduction and the diffusion, that must be parallelized to improve
the model performances.

The {\bf reproduction} stage consists in updating the total population
of each plot by taking the individuals arrived at the preceding
computation step. This stage involves processing the whole set of
cells of the environment to sum their population. The computed value
is recorded in the plot associated to each cell. This process can be
assimilated to a reduction operation on all the population cells
associated to one plot to obtain its population.

The {\bf diffusion} stage simulates the natural behavior of the
Collembola that tends toward occupying the whole space over time. This
stage consists in computing a new value for each cell depending on
the population of the neighbor cells. This process can be assimilated
to a linear diffusion at each iteration of the population of the cells
across their neighbors.

These two processes are quite common in numerical computations so that
the Collembola model can be adapted to a GPU execution without much
difficulty.

\subsection{Collembola implementation}

In the collembola simulator biodiversity is modeled by populations of
collembola individuals, which can reproduce and diffuse to favorable
new habitats. This is implemented as a fixed reproduction factor,
applied to the size of each population, followed by a linear diffusion
of each cell population to its eight neighbors. These reproduction and
diffusion processes are followed by two more steps on the GPU
implementation. The first one consist of culling of populations in an
inhospitable environment, by checking each cell value and terrain
type, and setting its population to zero if necessary.  The final
simulation step is the reduction of the cell populations for each
plot, to obtain an updated plot population for statistic
purposes. This separate computation step, done while updating each
cell population in the reference sequential algorithm, is motivated by
synchronization problems and allows the reduction of the total number
of memory accesses needed to updated those populations.

%\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
(Figure~\ref{ch17:fig:collem_algorithm}) as a straight mapping of each
cell to an OpenCL work-item (GPU thread). Listing
\ref{ch17:listing:collembola-diffuse} gives the kernel for the
diffusion implementation.  To prevent data coherency problems, the
diffusion step is split into two phases, separated by an execution
{\it barrier}. In the first phase each cell diffusion overflow is
calculated and divided by the number of neighbors. Note that, on the
border of the grid, populations can also overflow outside the
environment grid but we do not manage those external populations,
since there are no reason to assume our model to be isolated of its
surroundings. The overflow by neighbors value is stored for each cell
before encountering the barrier. After the barrier is met, each cell
reads the overflows stored by all its neighbors at the previous step
and applies them to its own population. In this manner, only one
barrier is required to ensure the consistency of population numbers,
since no cell ever modify a value other than its own.

Listing \ref{ch17:listing:collembola-reduc} gives the kernel for the
reduction implementation.  The only step requiring numerous
synchronized accesses is the reduction one: in this first approach, we
chose to use {\it atomic\_add} operation to implement this process,
but more efficient implementations using partial reduction and local
GPU memory could be implemented.

\pagebreak
\lstinputlisting[caption=collembola OpenCL reduction kernel,label=ch17:listing:collembola-reduc]{Chapters/chapter17/code/collem_kernel_reduc.cl}

\subsection{Collembola performance}

In this part we present the performance of the collembola model on
various CPU and GPU execution
platforms. Figure~\ref{ch17:fig:mior_perfs_collem} shows that the
number of cores and the processor architecture as a strong influence
on the obtained results

% : the
% dual-core processor equipping our Thinkpad platform has two to six
% longer executions times, compared to a six-core Phenom X6. 

% % \begin{figure}[h]
% %begin{minipage}[t]{0.49\linewidth}
% \centering \includegraphics[width=0.7\linewidth]{./Chapters/chapter17/figs/collem_perfs.pdf}
% \caption{Performance CPU et GPU du modèle Collemboles}
% \label{ch17:fig:mior_perfs_collem}
% %end{minipage}
% \end{figure}

% In figure~\ref{ch17:fig:mior_perfs_collem2} the Thinkpad curve is removed
% to make other trends clearer. Two more observation can be made, using
% this more detailled scale:

\begin{itemize}
\item Older GPU cards can be slower than modern processors. This can
  be explained by the new cache and memory access optimizations
  implemented in newer generations of GPU devices. These optimizations
  reduce the penalties associated with irregular and frequent global
  memory accesses. They are not available on our Tesla nodes.
\item GPU curves exhibit an odd-even pattern in their performance
  results. Since this phenomenon is visible on two distinct
  manufacturer hardware, driver, and OpenCL implementation, it is
  likely the result of the decomposition process based on warp of
  fixed, power-of-two sizes.
\item The number of cores is not the only determining factor: an Intel
  Core i7 2600K processor, even with only four cores, can provide
  better performance than a Phenom one.
\end{itemize}

\begin{figure}[h]
%begin{minipage}[t]{0.49\linewidth}
\centering
\includegraphics[width=0.7\linewidth]{./Chapters/chapter17/figs/collem_perfs_nothinkpad.pdf}
\caption{Performance of the Collembola model on CPU and GPU.}
\label{ch17:fig:mior_perfs_collem}
%end{minipage}
\end{figure}

Both graphs show that using the GPU to parallelize part of the
simulator results in tangible performance gains over a CPU execution
on modern hardware. These gains are more mixed on older GPU platforms
due to the limitations when dealing with irregular memory or execution
patterns often encountered in MAS systems. This can be closely linked
to the availability of caching facilities on the GPU hardware and its
dramatic effects depend on the locality and frequency of data
accesses. In this case, even if the Tesla architecture offers more
execution cores and is the far costlier solution, more recent,
cheaper, solutions such as high-end GPU provide better performance
when the execution is not constrained by memory size.

\section{Second example}
\label{ch17:sec:2ndmodel}

The second model, the MIOR model, simulates the behavior of microbian
colonies. Its execution model is more complex so that it requires
changes in the initial algorithm and the use of synchronization to
benefit from the GPU architecture.

\subsection{The MIOR model}
\label{ch17:subsec:miormodel}

The MIOR~\cite{C.Cambier2007} model was developed to simulate local
interactions in soil between microbial colonies and organic
matters. It reproduces each small cubic unit ($0.002 m^3$) of soil as
a MAS.

Multiple implementations of the MIOR model have already been
realized, in Smalltalk and Netlogo, in 2 or 3 dimensions. The last
implementation, used in our work and referenced as MIOR in the
rest of the chapter, is freely accessible online as
WebSimMior\footnote{http://www.IRD.fr/websimmior/}.

MIOR is based around two types of agents: (1) the Meta-Mior (MM),
which represents microbial colonies consuming carbon and (2) the
Organic Matter (OM) which represents carbon deposits occurring in
soil.

The Meta-Mior agents are characterized by two distinct behaviors:
\begin{itemize}
\item \emph{breath}: this action converts mineral carbon from the soil
  to carbon dioxide ($CO_{2}$) that is released into the soil and
\item \emph{growth}: by this action each microbial colony fixes the
  carbon present in the environment to reproduce itself (augment its
  size). This action is only possible if the colony breathing needs
  are covered, i.e., enough mineral carbon is available.
\end{itemize}

These behaviors are described in Algorithm~\ref{ch17:seqalgo}.

\begin{algorithm}[h]
\caption{evolution step of each Meta-Mior (microbial colony) agent} 
\label{ch17:seqalgo}
\KwIn{A static array $mmList$ of MM agents}
\myinput{A static array $omList$ of OM agents}
\myinput{A MIOR environment $world$}
$breathNeed \gets world.respirationRate \times mm.carbon$\;
$growthNeed \gets world.growthRate \times mm.carbon$\;
$availableCarbon \gets totalAccessibleCarbon(mm)$\;
\uIf{$availableCarbon > breathNeed$}{
  \tcc{ Breath }
  $mm.active \gets true$\;
  $availableCarbon \gets availableCarbon - consumCarbon(mm, breathNeed)$\;
  $world.CO2 \gets world.CO2 + breathNeed$\;
  \If{$availableCarbon > 0$}{
    \tcc{ Growth }
    $growthConsum \gets max(totalAccessCarbon(mm), growthNeed)$\;
    $consumCarbon(mm, growthConsum)$\;
    $mm.carbon \gets mm.carbon + growthConsum$\;
  }
}
\Else{
  $mm.active \gets false$
}
\end{algorithm}

Since this simulation takes place at a microscopic scale, a large
number of these simulations must be executed for each macroscopic
simulation step to model a realistic-sized unit of soil. This leads to
large computing needs despite the small computation cost of each
individual simulation.


\subsection{MIOR implementation}

As pointed out previously, the MIOR implementation implied more
changes for the initial code to be run on GPU.  As a first attempt, we
tried a simple GPU implementation of the MIOR simulator, with only
minimal changes to the CPU algorithm. Execution times showed the
inefficiency of this approach and highlighted the necessity of
adapting the simulator to take advantage of the GPU execution
capabilities~\cite{lmlm+12:ip}. In this part, we show the main changes
which were realized to adapt the MIOR simulator on GPU architectures.

\subsubsection{Execution mapping on GPU}

\begin{figure}
\centering
\includegraphics[width=0.7\textwidth]{Chapters/chapter17/figs/repartition.pdf}
\caption{Consolidation of multiple simulations in one OpenCL kernel execution.}
\label{ch17:fig:gpu_distribution}
\end{figure}

Each MIOR simulation is represented by a work-group, and each agent by
a work-item. A kernel is in charge of the life cycle process for each
agent of the model. This kernel is executed by all the work-items of
the simulation each on its own GPU core.

The usage of one work-group for each simulation allows the easy
execution of multiple simulations in parallel, as shown on
Figure~\ref{ch17:fig:gpu_distribution}.  By taking advantage of the
execution overlap possibilities provided by OpenCL, it then becomes
possible to exploit all the cores at the same time, even if an unique
simulation is too small to use all the available GPU cores. However,
the maximum size of a work-group is limited (to $512$), which allows
us to execute only one simulation per work-group when using $310$
threads (number of OM in the reference model) to execute the
simulation.

The usage of the GPU to execute multiple simulations is initiated by
the CPU. The CPU keeps total control of the simulator execution
flow. Thus, optimized scheduling policies (such as submitting kernels
in batch, limiting the number of kernels, or asynchronously retrieving
the simulation results) can be defined to minimize the cost related to
data transfers between CPU and GPUs.

\subsubsection{Data structures translation}
\label{ch17:subsec:datastructures}

The adaptation of the MIOR model to GPU requires the mapping of the
data model to OpenCL data structures. The environment and the agents
are represented by arrays of structures, where each structure
describes the state of one entity. The behaviors of these entities are
implemented as OpenCL functions to be called from the kernels during
execution.

Since the main program is written in Java, JOCL is responsible for the
allocation and mapping of the object data structures to OpenCL ones
before execution.

Four main data structures are defined: (1) an array of MM agents,
representing the state of the microbial colonies. (2) an array of OM
agents, representing the state of the carbon deposits. (3) a topology
matrix, which stores accessibility information between the two types
of agents of the model (4) a world structure, which contains all the
global input data (metabolism rate, numbers of agents) and output data
(quantity of $CO_{2}$ produced) of the simulation. The C-like OpenCL
structures used to represent each type of to agent and the environment
are illustrated in
(Figure~\ref{ch17:listing:mior_data_structures}). These data
structures are initialized by the CPU and then copied on the GPU.


\lstinputlisting[caption=main data structures used in a MIOR simulation,label=ch17:listing:mior_data_structures]{Chapters/chapter17/code/data_structures.cl}

The world topology is stored as a two-dimension matrix which
represents OM indexes on the abscissa and MM indexes on the
ordinate. Each agent walks through its line/column of the matrix at
each iteration to determinate which agents can be accessed during the
simulation.  Since many agents are not connected, this matrix is
sparse, which introduces a big number of useless memory accesses. To
reduce the impact of these memory accesses we use a compacted,
optimized representation of this matrix based
on~\cite{Gomez-Luna:2009:PVS:1616772.1616869}, as illustrated in
Figure~\ref{ch17:fig:csr_representation}. This compact representation
considers each line of the matrix as an index list, and only stores
accessible agents compactly, to reduce the number of non-productive
accesses.

\begin{figure}[h]
\centering
\includegraphics[width=0.8\textwidth]{Chapters/chapter17/figs/grid.pdf}
%\psfig{file=figs/grid, height=1in}
\caption{Compact representation of the topology of a MIOR simulation.}
\label{ch17:fig:csr_representation}
\end{figure}

Since dynamic memory allocation is not possible yet in OpenCL and is
only provided in the latest revisions of the CUDA standard, these
matrices are statically allocated. The allocation is based on the
worst-case scenario where all OM and MM are linked since the real
occupation of the matrix cells cannot be deduced without some kind of
preprocessing computations.

\subsubsection{Critical resources access management}
\label{ch17:subsec:concurrency}

One of the main concers in the MIOR model is to ensure that all the
microbial colonies will have an equitable access to carbon resources,
when multiple colonies share the same deposits. Access
synchronizations are mandatory in these cases to prevent conflicting
updates on the same data that may lead to calculation error (e.g. loss
of matter).

On massively parallel architectures such as GPUs, these kind of
synchronization conflicts can lead to an inefficient 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.

\pagebreak
\lstinputlisting[caption=main MIOR kernel,label=ch17:listing:mior_kernels]{./Chapters/chapter17/code/mior_kernels.cl}

From the sequential algorithm (Algorithm~\ref{ch17:seqalgo}) where all
the agents share the same data, we have developed a parallel algorithm
composed of three sequential stages separated by synchronization
barriers. This new algorithm is based on the distribution of the
available OM carbon deposits into parts at the beginning of each
execution step. The three stages, illustrated in
Listing~\ref{ch17:listing:mior_kernels}, are the following:

\begin{enumerate}
\item \emph{scattering}: the available carbon in each carbon deposit
  (OM) is equitably dispatched among all accessible MM in the form of
  parts,
\item \emph{live}: each MM consumes carbon in its allocated parts for
  its breathing and growing processes, and
\item \emph{gathering}: unconsumed carbon in parts is gathered back
  into the carbon deposits.
\end{enumerate}

This solution suppresses the data synchronization needed by the first
algorithm, thus the need for synchronization barriers, and requires
only one kernel launch from Java as described on
Listing~\ref{ch17:fig:mior_launcher}.

\lstinputlisting[caption=MIOR simulation launcher,label=ch17:fig:mior_launcher]{Chapters/chapter17/code/mior_launcher.java}

\subsubsection{Termination detection}

The termination of a MIOR simulation is reached when the model
stabilizes and no more $CO_{2}$ is produced. This termination
detection can be done on either the CPU or the GPU but it requires a
global view on the system execution.

In the first case, when the CPU controls the GPU simulation process,
the detection is done in two steps: (1) the CPU starts the execution
of a simulation step on the GPU and (2) the CPU retrieves the GPU data
and determines if another iteration must be launched or if the
simulation has terminated. This approach allows a fine-grain control
over the GPU execution, but it requires many costly data transfers as
each iteration result must be sent from the GPU to the CPU. In the
case of the MIOR model these costs are mainly due to the inherent
PCI-express port latencies rather than to bandwidth limitation since
data sizes remains rather small, on the order of few dozens of
Megabytes.

In the second case the termination detection is directly implemented
on the GPU by checking the amount of available carbon between two
iterations. The CPU does not have any feedback while the simulation is
running, but retrieves the results once the kernel execution is
finished. This approach minimizes the number of transfers between the
CPU and the GPU.

\subsection{Performance of MIOR implementations}
\label{ch17:subsec:miorexperiments}

In this part we present several MIOR GPU implementations using the
distribution/gathering process described in the previous section and
compare their performance on two distinct hardware platform, i.e., two
different GPU devices. Five incremental MIOR implementations were
realized with an increasing level of adaptation for the algorithm: in
all cases, we choose the average time over 50 executions as a
performance indicator.

\begin{itemize}
\item The \textbf{GPU 1.0} implementation is a direct implementation
  of the existing algorithm and its data structures where data
  dependencies were removed, and it uses the noncompact topology
  representation described in Section~\ref{ch17:subsec:datastructures}
\item The \textbf{GPU 2.0} implementation uses the previously
  described compact representation of the topology and remains
  otherwise identical to the GPU 1.0 implementation.
\item The \textbf{GPU 3.0} implementation introduces the manual
  copy into local (private) memory of often-accessed global data, such
  as carbon parts or topology information.
\item The \textbf{GPU 4.0} implementation is a variant of the GPU 1.0
  implementation but allows the execution of multiple simulations for
  each kernel execution.
\item The \textbf{GPU 5.0} implementation is a multisimulation
  version of the GPU 2.0 implementation, using the execution of
  multiple simulations for each kernel execution as for GPU 4.0.
\end{itemize}

The two last implementations \textbf{GPU 4.0} and \textbf{GPU 5.0}
illustrate the gain provided by a better usage of the hardware
resources, thanks to the driver execution overlapping capabilities. A
sequential version of the MIOR algorithm, labeled \textbf{CPU}, is
included for comparison purpose. This sequential version was developed
in Java, the same language used for GPU implementations.

For these performance evaluations, two platforms are used. The first
one is representative of the kind of hardware which is available on
HPC clusters. It is a cluster node dedicated to GPU computations with
two Intel X5550 processors running at $2.67$GHz and one Tesla C1060
GPU device running at $1.3$GHz and composed of $240$ cores ($30$
multiprocessors). The second platform illustrates what can be
expected from a personal desktop computer built a few years ago. It
uses an Intel Q9300 CPU, running at $2.5$GHz, and a Geforce 8800GT GPU
running at $1.5$GHz and composed of $112$ cores ($14$
multiprocessors). The purpose of these two platforms is to assess the
benefit that could be obtained when a scientist has access either to
specialized hardware as a cluster or tries to take advantage of its
own personal computer.

\begin{figure}[!h]
%begin{minipage}[t]{0.49\linewidth}
\centering
\includegraphics[width=0.7\linewidth]{Chapters/chapter17/figs/mior_perfs_tesla.pdf}
%\caption{Performance CPU and GPU sur carte Tesla C1060}
\caption{CPU and GPU performance on a Tesla C1060 node.}
\label{ch17:fig:mior_perfs_tesla}
%end{minipage}
\end{figure}

Figures~\ref{ch17:fig:mior_perfs_tesla}~and~\ref{ch17:fig:mior_perfs_8800gt}
show the execution time for $50$ simulations on the two hardware
platforms. A size factor is applied to the problem: at scale 1, the
model contains $38$ MM and $310$ OM, while at the scale 6 these
numbers are multiplied by six. The size of the environment is modified
as well to maintain the same average agent density in the model. This
scaling factor displays the impact of the chosen size of simulation on
performance.

%hspace{0.02\linewidth}
%begin{minipage}[t]{0.49\linewidth}
\begin{figure}[!h]
\centering
\includegraphics[width=0.7\linewidth]{Chapters/chapter17/figs/mior_perfs_8800gt.pdf}
\caption{CPU and GPU performance on a personal computer with a Geforce 8800GT.}
\label{ch17:fig:mior_perfs_8800gt}
%end{minipage}
\end{figure}

The charts show that for small problems the execution times of all
the implementations are very close. This is because the GPU execution
does not have enough threads (representing agents) for an optimal
usage of GPU resources. This trend changes around scale $5$ where GPU
2.0 and GPU 3.0 take the advantage over the GPU 1.0 and CPU
implementations. This advantage continues to grow with the scaling
factor, and reaches a speedup of $10$ at the scale $10$ between the
fastest single-simulation GPU implementation and the first, naive one
GPU 1.0.

Multiple trends can be observed in these results. First, optimizations
for the GPU hardware show a large, positive impact on performance,
illustrating the strong requirements on the algorithm properties to
reach execution efficiency. These charts also show that despite the
vast difference in numbers of cores between the two GPU platforms, the
same trends can be observed in both cases. We can therefore expect
similar results on other GPU cards, without the need for more
adaptations.

\begin{figure}[!h]
\centering
\includegraphics[width=0.7\linewidth]{Chapters/chapter17/figs/monokernel.pdf}
\caption{Execution time of one multi-simulation kernel on the Tesla
  platform.}
\label{ch17:fig:monokernel_graph}
\end{figure}

\begin{figure}[!h]
\centering
\includegraphics[width=0.7\linewidth]{Chapters/chapter17/figs/multikernel.pdf}
\caption{Total execution time for 1000 simulations on the Tesla
  platform, while varying the number of simulations for each kernel.}
\label{ch17:fig:multikernel_graph}
\end{figure}

There are two ways to measure simulations performance: (1) by
executing only one kernel, and varying its size (the number of
simulations executed in parallel), as shown in
Figure~\ref{ch17:fig:monokernel_graph}, to test the costs linked to
the parallelization process or (2) by executing a fixed number of
simulations and varying the size of each kernel, as shown in
Figure~\ref{ch17:fig:multikernel_graph}.

Figure~\ref{ch17:fig:monokernel_graph} illustrates the execution time
for only one kernel. It shows that for small numbers of simulations
run in parallel, the compact implementation of the model topology is
faster than the two-dimension matrix representation. This trends
reverse with more than $50$ simulations in parallel, which can be
explained either by the nonlinear progression of the synchronization
costs or by the additional memory required for the access-efficient
representation.

Figure~\ref{ch17:fig:multikernel_graph} illustrates the execution time
of a fixed number of simulations. It shows that for a small number of
simulations run in parallel, the costs resulting from program setup,
data copies, and launch on GPU are very detrimental to
performance. Once the number of simulations executed for each kernel
grows, these costs are counterbalanced by computation costs. This
trend is more marked in the case of the sparse implementation (GPU
4.0) than in the compact one but appears on both curves. With more
than $30$ simulations for each kernel, execution times stall, since
hardware limits are reached. This indicates that the cost of preparing
and launching kernels become negligible compared to the computing time
once a good GPU occupancy rate is achieved.

\section{Analysis and recommendations}
\label{ch17:analysis}

In this section we synthesize the observations done on the two models
and identify some recommendations for implementing complex systems on
GPU platforms.

\subsection{Analysis}

In both the collembola and the MIOR model, a critical problematic is
the determination of the parts of the simulation that are to be run on
GPU and which are to remain on the CPU. The determination of these
parts is a classic step of any algorithm parallelization and must take
into account considerations such as the cost of the different parts of
the algorithm and the expected gains.

In the case of the collembola model two steps of the algorithm were
ported to GPU. Both steps use straightforward, easily parallelizable
operations where a direct gain can be expected by using more execution
cores without important modifications to the algorithm.

In the MIOR model case, however, no such inherently parallelizable
parts are evident in the original sequential algorithm. This is mainly
explained by the rate of interactions between agents in this model in
the form of two operations (breathing, growth) using heavily-shared
carbon resources. In this case the algorithm had to be more profoundly
modified while keeping in mind the need to remain true to the original
model, to synchronize the main execution step of all agents in the
model, to ensure equity, and to minimize the numbers of
synchronizations. The minimization is done by factoring the
distribution of carbon in the model in two separated steps at the
beginning and the end of each iteration rather than at multiple points
of the execution.

\subsection{MAS execution workflow}

Many MAS simulations decompose their execution process into discrete
evolution steps where each step represents a quantum of time (minimal
unit of time described). At the end of each step many global data,
graphical displays or output files are updated. This execution model
may not correctly fit on GPU platforms as they assume more or less a
batch-like workflow model. The execution model must be split into the
following ever repeating steps:

\begin{itemize}
\item Allocation of GPU data buffers
\item Copy of data from CPU to GPU
\item GPU kernels execution
\item Copy of results from GPU to CPU
\end{itemize}

This workflow works well if the considered data transfer time is
negligible compared to GPU execution or can be done in parallel,
thanks to the asynchronous nature of OpenCL. If we are to update the
MAS model after each iteration then performance risks being
degraded. This is illustrated in the MIOR model by the fact that the
speedup observed on GPU is much more significant for bigger
simulations, which imply longer GPU execution times. Our solution to
this problem is to desynchronize the execution of the MAS model and
its GPU parts by requesting the execution of multiple steps of the GPU
simulations for each launch.

Another related prerequisite of GPU execution is the ability to have
many threads executed, to allow an efficient exploitation of the
superior number of cores provided by the architecture. In the case of
MAS models, this means that executing one agent at a time on GPU is
meaningless in regard to GPU usage, copying cost, and actual gain in
execution time, if the agent computations are not complex enough. In
the MIOR and the collembola models, this is solved by executing the
computations for all agents of the model at the same time. If the
model has only chronic needs for intensive computations, then some
kind of batching mechanism is required to store waiting treatments in
a queue, until the total sum of waiting computations justifies the
transfer cost to the GPU platform.

\subsection{Implementation challenges}

Besides the execution strategy challenges described above, some
implementation challenges also occur when implementing an OpenCL
version of a MAS model, mainly related to the underlying limitations
of the execution platform.

The first one is the impossibility (except in latest CUDA versions) to
dynamically allocate memory during execution. This is a problem in the
case of models where the number of agents can vary during the
simulation, such as prey-predator models. In this case, the only
solution is to overestimate the size of arrays or data structures to
accommodate these additional individuals, or to use the CPU to resize
data structures when these situations occur. Both approaches require
trending either memory or performance and are not always practical.

Another limitation is the impossibility to store pointers in data
structures, since OpenCL only allows one-dimension static arrays. This
precludes the usage of structures such as linked-list, graphs or
sparse matrices not represented by some combination of static arrays,
and can be another source of memory or performance losses.

In the case of MIOR, this problem is especially exacerbated in the
case of neighboring storage: both representations consume much more
memory than is actually required, since the worst case (all agents
have access to all others agents) has to be taken into account when
dimensioning the data structure.

The existence of different generations of GPU hardware is also a
challenge. Older implementations, such as the four year old Tesla
C1060 cards, have very strong constraints in term of memory accesses
and requires very regular access patterns to perform efficiently. MAS
having many random accesses, such as MIOR, or many small global memory
accesses, such as Collembola, are penalized on these older
cards. Fortunately, these requirements are less present is modern
cards, which offer caches and other facilities traditionally present
on CPU to offset these kinds of penalties.

The final concern is related to the previous ones and often results in
more memory consumption. The amount of memory available on GPU cards
is much more limited and adding new memory capabilities is more costly
compared to expending a CPU RAM. On computing clusters, hardwares
nodes with 128GB of memory or more have become affordable, whereas
newer Tesla architecture remains limited to 16GB of memory. This can
be a concern in the case of big MAS models, or small ones which can
only use memory-inefficient OpenCL structures.

\subsection{MCSMA}
\label{ch17:Mcsma}

As shown in the previous sections, many data representation choices
are common to entire classes of MAS. The paradigm of grid, for
example, is often encountered in models where each cell constitutes
either the elementary unit of simulation
(SugarScape~\cite{Dsouza2007}) or a discretization of the environment
space (Pathfinding~\cite{Guy09clearpath}). These grids can be
considered as two- or three-dimensional matrices, whose processing can
be directly distributed.

Another common data representation encountered in MAS system is the
usage of 2D or 3D coordinates to store the position of each agent of
the model. In this case, even if the environment is no longer
discrete, the location information still imply computations (Euclidean
distances) which can be parallelized.

MCSMA~\cite{lmlm+13:ip} is a framework developed to provide to the MAS
designer those basic data structures and the associated operations, to
facilitate the portage of existing MAS on GPU. Two levels of
utilization are provided to the developer, depending on its usage
profile:

\begin{itemize}
\item A high-level library, composed of modules regrouping classes of
  operations. Such operation can distance computations in 1D, 2D or 3D
  grids, diffusion or reduction operations on matrices...
\item A low-level API which allows the developer direct access to the
  GPU and the inner working of MCSMA, to develop new modules in the
  case where the required operations are not yet provided by the
  platform.
\end{itemize}

Both usage levels were illustrated in the above two practical
cases. In MIOR, the whole algorithm (baring initialization) is ported
on GPU as a specific plugin which allows executing $n$ MIOR
simulations and retrieving their results. This first approach requires
extensive adaptations to the original algorithm.  In collembola, to
the contrary, the main steps of the algorithm remain executed on the
CPU, and only specific operations are delegated to generic, already
existing diffusion and reduction kernels. The fundamental algorithm is
not modified and GPU execution is only applied to specific parts of
the execution which may benefit from it. These two programming
approaches allow incremental adaptations of existing Java MAS to
accelerate their execution on GPU, while retaining the option to
develop their own reusable or more efficient module to supplement the
already existing ones.

\section{Conclusion}
\label{ch17:conclusion}

This chapter has addressed the issue of complex system simulation by
using agent-based paradigms and GPU hardware. From the experiments on
two existing agent-based models of soil science we have provided
useful information on the architecture, the algorithm design, and the
data management to run agent-based simulations on GPU, and more
generally to run computationally intensive applications that are not
based on purely-matricial models. The first result of this work is
that adapting the algorithm to a GPU architecture is possible and
suitable to speed up agent based simulations as illustrated by the
MIOR model. Coupling CPU with GPU seems to be an interesting way to
take better advantage of the power given by computers and clusters as
illustrated by the collembola model. From our point of view the
adaptation process is less costy in time than a traditional
parallelization on distributed nodes and not much difficult than a
standard multithreaded parallelization, since all the data remains on
the same host and can be shared in central memory. The usage of OpenCL
also enables a portable simulator that can be run on different
graphical units. Even using a mainstream card such as the GPU card of
a standard computer can lead to significant performance
improvements. This is an interesting result as it opens up the field
of inexpensive HPC to a large community.

In this perspective, we are working on MCSMA, a development platform
that would facilitate the use of GPU or many-core architectures for
multi-agent simulations. Our first work has been the definition of
common, efficient, reusable data structures, such as grids or
lists. Another goal is to provide easier means to control the
distribution of specific processes between CPU or GPU, to allow the
easy exploitation of the strengths of each platform in the same
multi-agent simulation. We think that the same approach, i.e.,
developing specific environments that facilitate the developer access
to the GPU power, can be applied in many domains with computationally
intensive needs to open the GPU use to a larger community.

\putbib[Chapters/chapter17/biblio]