1. An Parallel Multi-Agent Spatial Simulation Environment for Cluster Systems
Thank you, chairman for the kind introduction.
And hello, everyone.
Today, I am going to present our on-going project that implement and practicalize an agent-based workbench for on-the-fly sensor-data analysis, more specifically for orchard temperature prediction and crop monitoring.
Munehiro Fukuda, Ph.D.
Computing & Software Systems University of Washington Bothell
December 4
IEEE CSE 2013, Sydney

2. Research Motivation Agent-based or entity-based simulations:
Simulate the emergent collective behavior of social or biological agents.
Require a mega number of agents for the reality.
Most parallelization
Has been limited to shared-memory environments (OpenMP, CUDA, etc.) but not cluster systems.
Challenges
Machine awareness
Ghost space management
Agent management (e.g., cloning, migration, and termination)
Fine-grained execution
MASS: a parallel library for multi-agent spatial simulation for cluster systems
December 4
IEEE CSE 2013, Sydney

3. Outline Related work MASS execution model and library spec.
Programmability analysis
Spatial simulation
Multi-agent simulation
Performance evaluation
Conclusions
December 4
IEEE CSE 2013, Sydney

4. Related Work Distributed Arrays Multi-Agents
Examples: UPC, Co-Array Fortran, and Global Array.
Limited scalability versus limited remote memory access
Read-only ghost space
Examples: PDES-MAS, Nomadic Threads, and Repast HPC
Fine granularity versus intelligence
Hawk’s versus individual’s viewpoint
System-level agent management required
Loop parallelization goo enough?
for ( int I = 0; I < nAgents; i++ ) {
agents[i].updateStatus( );
agents[i].interactNeighbors( ); }
What if agents are introduced?
Node 1
Node 2
Node 3
December 4
IEEE CSE 2013, Sydney

5. MASS Library Execution Model
Y-axis
A Bag of Agents
Agents
Agents
Agents
Application Layer
(x,y)
Places
X-axis
Thread 0
Thread 1
Thread 2
Thread 3
Thread 0
Thread 1
Thread 2
Thread 3
Thread 0
Thread 1
Thread 2
Thread 3
Process Rank 0
socket
Process Rank 1
socket
Process Rank 2
MASS Library Layer
CPU Core 0
CPU Core 1
CPU Core 2
CPU Core 3
CPU Core 0
CPU Core 1
CPU Core 2
CPU Core 3
CPU Core 0
CPU Core 1
CPU Core 2
CPU Core 3
System Memory
System Memory
System Memory
mnode0.uwb.edu
mnode1.uwb.edu
LAN
mnode2.uwb.edu
December 4
IEEE CSE 2013, Sydney

6. MASS Library Specification
Public static void main( String[ ] args ) {
MASS.init( args );
Places space = new Places( handle, “MySpace”, params, xSize, ySize);
Agents agents = new Agents( handle, “MyAgents”, params, space, population );
space.callAll( MySpace.func1, params );
space.exchangeAll( MySpace.func2, neighbors );
agents.exchangeAll( MyAgents.func3 );
agents.manageAll( );
MASS.finish( ); }
func1( ) …
func2( )
func3( )
December 4
IEEE CSE 2013, Sydney

7. MASS Library Implementation
Master node
main( String[] args ) {
MASS.init( );
MASS.finish( ); }
JSCH or libssh2
JSCH or libssh2
Slave node 2
Slave node 1
December 4
IEEE CSE 2013, Sydney

8. Programmability Analysis Spatial Simulation – Wave2D (Implemented)
December 4
IEEE CSE 2013, Sydney

9. Programmability Analysis Spatial Simulation – BrainGrid (Abstract Code)
December 4
IEEE CSE 2013, Sydney

10. Programmability Analysis Multi-Agent Simulation – Random Walk
December 4
IEEE CSE 2013, Sydney

11. Programmability Analysis Multi-Agent Simulation – FluTE (Abstract Code)
Infected
person
communities
Contagious
The original work from Univ. New Mexico:
December 4
IEEE CSE 2013, Sydney

12. Programmability Analysis Agent-Based Data Analysis – Biological Network Motif
December 4
IEEE CSE 2013, Sydney

13. MASS Programmability Spatial Simulation
Machine-unaware data distribution and collection
No explicit ghost space management
No explicit for-loop parallelization
Multi-Agent Simulation
Automated agent migration
Agent collision avoidance
Agent propagation and distributed termination
December 4
IEEE CSE 2013, Sydney

14. Performance Evaluation Place and Agent Granularity
Places Size
Agent Group Size
December 4
IEEE CSE 2013, Sydney

15. Performance Evaluation Spatial Simulation – Wave2D
December 4
IEEE CSE 2013, Sydney

16. Performance Evaluation Multi-Agent Simulation – Random Walk
December 4
IEEE CSE 2013, Sydney

17. MASS Execution Performance
The minimum conditions to benefit from MASS (Java version):
500 x 500 places
300,000 agents
100 floating-point operations / exchangeAll( ) or callAll( ), both returning values
December 4
IEEE CSE 2013, Sydney

18. Status and Future Plan More version and tools in development:
MASS C++
MASS Cuda
Parallel NetCDF reader/writer with MASS
Practical applications in our plan:
BrainGrid
FluTE
Biological network motif search
Climate change detection
December 4
IEEE CSE 2013, Sydney