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Managing Flocking Objects with an Octree Spanning a Parallel Message-Passing Computer Cluster.


Abstract and Figures

We investigate the management of fl ocking mobile objects using a parallel message-passing computer cluster. An o ctree, a da ta structure well-known for use in managing a 3D space, is adapted to "span" the cluster. Objects are distributed in the tree, and partitions of t he tree are distributed among the processors in such a way that a minimum of global information is required to b e shared b y the processors. When ob jects move, the tree is modified a ccordingly; this in turn may c ause partitions to migrate processors. Two constraints drive the distribution algorithm: (1) minimizing message traffic by clustering nearby objects on the same processor, and (2) processor load-balancing. Boids, flocking a rtificial life forms, embody the objects in this s tudy. The performance of t he system is measured in terms of t he inter-processor message traffic as a function o f t he number, interactivity, and mobility of objects. An application of the scheme allows external clients to view objects in specified spatial loci.
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Managing Flocking Objects with an Octree Spanning a Parallel Message-Passing
Computer Cluster
Thomas E. Portegys, Kevin M. Greenan
Illinois State University,
We investigate the management of flocking mobile
objects using a parallel message-passing computer
cluster. An octree, a data structure well-known for use
in managing a 3D space, is adapted to “span” the
cluster. Objects are distributed in the tree, and partitions
of the tree are distributed among the processors in such
a way that a minimum of global information is required
to be shared by the processors. When objects move, the
tree is modified accordingly; this in turn may cause
partitions to migrate processors. Two constraints drive
the distribution algorithm: (1) minimizing message traffic
by clustering nearby objects on the same processor, and
(2) processor load-balancing. Boids, flocking artificial
life forms, embody the objects in this study. The
performance of the system is measured in terms of the
inter-processor message traffic as a function of the
number, interactivity, and mobility of objects. An
application of the scheme allows external clients to view
objects in specified spatial loci.
Keywords: message-passing parallel computer, octree,
flocking behavior, boids.
1. Introduction
Many systems involve large numbers of mobile and
spatially related components. Examples include
simulating molecular changes in chemical reactions,
weather modeling, air and fluid dynamics, population
modeling, forest fire simulation, and networked gaming.
These systems require the sort of massive computational
power that parallel processors can provide in an
economical fashion.
We chose a message-passing parallel computer cluster
as our platform. Message-passing machines are typically
less efficient than shared-memory processors for tasks
involving relatively small numbers of tightly connected
objects, and in turn show superior performance and
scaling for tasks involving large numbers of loosely
coupled objects [8]. One of the aims of this project is to
investigate the conditions under which the use of a
message-passing system is effective. To this end, we
measure the performance of the system in terms of the
inter-processor message traffic as a function of the
number of objects and their mobility.
A number of investigations of N-body tasks on
message-passing parallel clusters appear in the literature
[3,4,7]. In our study, boids [6], flocking artificial life
forms, are used as test objects. A boid moves about in a
swarming fashion that requires knowledge other boids in
its vicinity. This produces group flocking movements that
are characteristic of animal and human behavior in
contrast to typical N-body movements caused by force-
fields. The movement of flocks across processor
boundaries provides an opportunity to study the
processing and message-passing capabilities of the
system, as well as to investigate the effectiveness of load-
1.1. Spanning octree
An octree, a data structure well-known for use in
managing 3D space, is adapted to “span” the cluster. In
our octree, space is recursively divided into smaller and
cubic partitions such that terminal cubes usually contain
a single object. The tree is partitioned by orthogonally
bisecting space and assigning volumes to each processor
in the form of bounds. Figure 1 depicts a 2D (quadtree)
view of a tree spanning a space containing a number of
Figure 1. Initial Spanning Tree
In the figure, the partition bounds are globally known
to processors P0 through P3. This is accomplished by
replicating the bounds so that each processor has its own
copy. The goal is to provide a fast determination of which
partitions of the octree are managed by which processor
so that objects in the corresponding volumes of space can
be accessed. Each processor manages a single volume of
space and the objects that it contains. The specific details
of the objects in these local spaces, including the
configurations of the sub-trees that track them, are
known only to each processor.
1.2. Migrating objects
Figure 2 shows objects O1 and O2 migrating to the
space owned by processor P3. This involves at a
minimum messages from P2 to P3 to insert the migrating
objects in the target space. P2 then deletes its copy of the
Figure 2. Migrating objects
1.3. Load-balancing
Two constraints drive the distribution algorithm: (1)
minimizing message-passing by clustering nearby objects
on the same processor, and (2) processor load-balancing.
Figure 3 shows the result of load-balancing that has
caused the global space to be repartitioned as a result of
the migration of objects O1 and O2. This has caused the
redistribution of objects O3 and O4 to P2, and forced an
update to the global bounds. We use a variation of an
orthogonal recursive bisection algorithm [5] to partition
Figure 3. Repartitioned space
1.4. Object interaction
In contrast with typical N-body systems, a boid does
not often interact with every other boid. A boids
movement depends only on the movements and positions
of nearby boids. Because of this, a straightforward octree
search is sufficient to implement efficient proximity
checking, as opposed to using a cumulative scheme such
as the multipole method [1,2].
Figure 4 illustrates a situation in which objects O1
and O3 reside in space managed by processor P0, and
object O2 resides in space managed by P1. Consider
proximity checking for O1. In the case of an O1-O3
interaction, the checking can be entirely local to P0.
However, since O1 extends into space owned by P1, and
the contents of this space are unknown to P0, a message
to P1 must be sent containing O1’s position so that P1
may conduct the proximity checking in its local space.
Figure 4. Proximity checking
1.5. Viewports
An application of the scheme allows external clients
to view objects in specified spatial loci. This is
accomplished by designating one of the processors as a
viewport gateway. Alternatively, a dedicated machine
may perform the gateway function. Clients connecting to
the gateway specify a viewing frustum, which is
essentially a bounding box. The gateway then makes
appropriate searches on the various processors to obtain
information about objects contained in the viewing
frustum. A list of these is returned to the client, allowing
a graphical view of a volume of space to be rendered.
2. Procedure
Our platform is the Applied Computer Science
Department’s Beowulf machine, a cluster of 16 SUN
Ultra 10 workstations running SuSe Linux, connected by
a 10mbps Ethernet. The software is the C++
programming language and PVM (Parallel Virtual
Machine) to provide the message-passing infrastructure.
The OpenGL graphics language is used to exercise the
viewport feature.
The partition algorithm requires that the number of
processors be a power of 8, yet for obvious reasons this is
not a practically achievable machine configuration. The
solution was to implement processors as virtual entities
and distribute processors among physical machines in a
clustered manner.
The boids code was initially obtained from an internet
source. After a measure of re-writing and parameter
tuning we obtained satisfactory flocking behavior: a
variety of dynamically changing flock sizes.
A PVM program is a master-slave process
configuration. Our master process spawned slave
processes representing virtual processors on specified
machines, initializing them with their bounds and a
random distribution of boids.
Updating processor bounds for load-balancing is done
by the master using information from the slave
processors. Each processor reports its current load
(number of boids) and the position of the median boid.
The master then re-partitions based on these weighted
boid positions.
After initialization, the master enters a loop, an
iteration of which constitutes the following cycle:
1. Broadcast an aim message to slave processors
causing them to determine the next position of each
boid. This entails intra-processor and cross-processor
searches not involving the master.
2. Broadcast a move message, causing processors to
move boids to their new positions, possibly involving
cross-processor insertions and deletions, also not
involving the master. Insertions are unacknowledged
for efficiency reasons.
3. If load-balancing, broadcast a report message,
causing the processors to send back their load and
median information. The master computes new
bounds and broadcasts them in a balance message.
4. If gathering statistics, broadcast a stats messages and
gather results.
5. If in viewing mode, broadcast the viewing planes in
a view message and gather the results. Each
processor determines which searches its octree for
boids falling within the viewing box.
The cycle steps are synchronized; each step is
completed before moving to the next. This means that the
master waits for all processors to respond before issuing
the next command. This can only happen after all
searching and insertion activity for a particular step is
completed by the slaves.
The viewing capability is implemented as a separate
thread within the master process. This allows a user to
navigate through space, selectively viewing boids, or to
run in non-interfering “blind” mode.
3. Results
Figure 5 is a graphical depiction of a simulation of
the system. Here, a number of mobile point objects are
shown in their octree volumes.
Figure 5. Octree simulation
The independent variables were: number of boids (25,
50, 100, 200), number of machines (4, 8), and load-
balancing (on/off). Unfortunately, due to hardware
problems we were not able to use the entire 16 processor
cluster. The dependent variables for which data was
gathered were: load (boids) per machine and message
traffic. Each trial was run for 1000 cycles. The boids had
an interaction range of 5 units. As the number of boids
increased, the spatial dimension were increased: 25 boids
in a 15x15x15 volume, 50 in 20x, 100 in 25x, and 200 in
Figures 6 and 7 show the average and standard
deviation boid load for 8 and 4 machine configurations,
respectively, under no load-balancing (NLB), and load-
balancing (LB) conditions. The flocking aspect of the
boids can be seen in the non-uniform distribution
indicated by the standard deviation.
Figure 6. Load for 8 machines
Figure 7. Load for 4 machines
Figures 8 and 9 show the message traffic for the 8 and
4 machine configurations. Notable here is the effect of
load-balancing, which causes a significant decrease in
message traffic, although not a correspondingly large
decrease in burstiness as indicated by the standard
Figure 8. Message traffic for 8 machines
Figure 8. Message traffic for 4 machines
4. Conclusion
The overall observation is that load-balancing can be
effective in reducing message traffic for flocking objects.
The cost for this improvement in our scheme is an
additional 2 steps in the processing cycle.
For future work, we propose to investigate
decentralized load-balancing schemes to avoid the
overhead cost. In addition, processor load could consist
of factors other than simple numbers of objects. For
example, the state of objects may be a viable factor. In a
forest fire simulation, burning areas would take more
computation resources, and thus these objects might
“weigh” more heavily. In addition, in a cluster of
heterogeneous processors, the resources of each processor
could be taken into account for load-balancing.
The code is available at:
5. Acknowledgements
The authors wish to especially thank Chris McBride
for many valuable ideas and contributions. Thanks also
to Andy Thayer and Tesh Shah for their insights.
6. References
[1] Anderson, C. R., “An implementation of the fast
multipole method without multipoles”, SIAM J. Sci. Stat.
Comput. 13 (1992) 923.
[2] Greengard, L. Gropp, W.L., "A Parallel version of the
Fast Multipole Method", Parallel Processing for
Scientific Computing SIAM Conference proceedings,
p213-222, 1988.
[3] Hariharan, B. and Aluru, S., “Efficient Parallel
Algorithms and Software for Compressed Octrees with
Applications to Hierarchical Methods”, High
Performance Computing - HiPC 2001 8th International
Conference, Hyderabad, India, December, 17-20, 2001.
[4] Hu, Y., “Implementing O(N) N-body algorithms
efficiently in data parallel languages (High Performance
Fortran)”, Journal of Scientific Programming, 1994.
[5] Salmon, J.K., “Parallel Hierarchical N-Body
Methods”, Ph.D. thesis, California Institute of
Technology, 1990.
[6] Scientific American: Feature Article: “Boids of a
Feather Flock Together”, November 2000.
[7] Sun Y., Liang, Z., and Wang, C-L, “A Distributed
Object-Oriented Method for Particle Simulations on
Clusters”, Proceedings of the 7th International
Conference on High Performance Computing and
Networking (HPCH Europe 1999), April 12-14, 1999,
Amsterdam, The Netherlands, 1999.
[8] Wilkinson, B. and Allen M., “Parallel Programming,
Techniques and Applications Using Networked
Workstations and Parallel Computers”, Prentice-Hall,
Inc., 1999.
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