a study on the parallelization of terrain-covering ant robots
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A Study on the Parallelization of Terrain-Covering AntRobots Simulations
Alessandro PellegriniFrancesco Quaglia
High Performance and DependableComputing Systems Group
Sapienza, University of Rome
PADABS 2013
Motivations
• Agent-based models reliably express interactions between differentobjects/entities in real world phenomena
• In some application domains, simulation timeliness is critical◦ Time-critical decision via what-if analysis (e.g., agent-based models in
disaster-rescue contexts)
• Agent-based simulations are useful to study steady state orequilibrium properties of a system
• What if models are used to determine the exact simulated-timewhen a given global predicate becomes true?
2 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Motivations (2)
• Traditional sequential simulation is perfect for fine grain inspectionof predicates
• . . . yet it doesn’t scale!
• . . . and models are larger everyday!!
• Parallel/Distributed simulation provides great performance
• Fine grain inspection is not viable◦ Process coordination is required◦ This hampers the achievable speedup
• Speculative (optimistic) simulation inserts an additional delay◦ Inspection is delayed until a portion of the computation becomes
committed
3 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Motivations (2)
• Traditional sequential simulation is perfect for fine grain inspectionof predicates
• . . . yet it doesn’t scale!
• . . . and models are larger everyday!!
• Parallel/Distributed simulation provides great performance
• Fine grain inspection is not viable◦ Process coordination is required◦ This hampers the achievable speedup
• Speculative (optimistic) simulation inserts an additional delay◦ Inspection is delayed until a portion of the computation becomes
committed
3 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Motivations (2)
• Traditional sequential simulation is perfect for fine grain inspectionof predicates
• . . . yet it doesn’t scale!
• . . . and models are larger everyday!!
• Parallel/Distributed simulation provides great performance
• Fine grain inspection is not viable◦ Process coordination is required◦ This hampers the achievable speedup
• Speculative (optimistic) simulation inserts an additional delay◦ Inspection is delayed until a portion of the computation becomes
committed
3 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Motivations (2)
• Traditional sequential simulation is perfect for fine grain inspectionof predicates
• . . . yet it doesn’t scale!
• . . . and models are larger everyday!!
• Parallel/Distributed simulation provides great performance
• Fine grain inspection is not viable◦ Process coordination is required◦ This hampers the achievable speedup
• Speculative (optimistic) simulation inserts an additional delay◦ Inspection is delayed until a portion of the computation becomes
committed
3 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Motivations (2)
• Traditional sequential simulation is perfect for fine grain inspectionof predicates
• . . . yet it doesn’t scale!
• . . . and models are larger everyday!!
• Parallel/Distributed simulation provides great performance
• Fine grain inspection is not viable◦ Process coordination is required◦ This hampers the achievable speedup
• Speculative (optimistic) simulation inserts an additional delay◦ Inspection is delayed until a portion of the computation becomes
committed
3 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F
F F
F
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T
F F T
F T
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T
F F T
F T
exact
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T T T
F F T T T
F T T T
exact
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T T T
F F T T T
F T T T
exact GVT
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T T T
F F T T T
F T T T
exact GVT
shift
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
The Completion-Shift Problem
LPi
LPi
LPi
F
F
F F F T T T
F F T T T
F T T T
exact GVT
shift
How does this shift affects the results?What is the tradeoff between performance and results’ reliability?
4 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Simulation Model: Terrain-Covering Ant Robots
• Original model by Koenig–Liu [KL01], we propose a parallel version
• Interesting model for the assessment of rescue scenarios
• The terrain is modeled as an undirected graph
• Space is divided into hexagonal cells
• An ant robot can move to adjacent cells, accounting for its speed(50 cm/s at most)
S
S
S
S
5 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Simulation Model: Terrain-Covering Ant Robots
• Original model by Koenig–Liu [KL01], we propose a parallel version
• Interesting model for the assessment of rescue scenarios
• The terrain is modeled as an undirected graph
• Space is divided into hexagonal cells
• An ant robot can move to adjacent cells, accounting for its speed(50 cm/s at most)
S
S
S
S
5 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Simulation Model: Terrain-Covering Ant Robots (2)
• Ant robots leave pheromones when passing through a cell
• Pheromones notify other robots of their visit to a cell
• A node-counting algorithm allows to select the least-visited nodewhen choosing direction
• Model’s events are:◦ REGION IN: an ant robot enters a given cell, trail counter is increased◦ UPDATE NEIGHBORS: adjacent LPs are notified of new trail counter
value◦ REGION OUT: an ant robot is leaving a given cell
6 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Simulation Model’s Configuration
• Square region, 12 Km2
• 4900 hexagonal cells (each on a LP) on 32 ROOT-Sim kernelinstances
• 4 source points (at region corners)
• Variable number of robots [4, 32] per source point
• Variable GVT computation interval [1, 5] seconds
• Simulation is run until full region coverage, visit factor of 20
• Comparison with a sequential run relying on a Calendar Queue
• 32-core HP ProLiant server, NUMA architecture
• 64 Gb RAM
• Linux Kernel 2.6.32-5-amd64 – Debian 6
7 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Reference Simulation Platform: ROOT-Sim
ROOT SIM
• Parallel simulation has been run on top of the ROme OpTimisticSimulator (ROOT-Sim)
• An Optimistic (Distributed) Parallel Discrete Simulation Platform
• Supports ANSI-C for simulation models’ development
• Transparently supports rollback via log/restore facilities
8 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Experimental Results: Simulation Completion Time
• Results averaged over 20 runs with different initial random seeds
• The simulation completion time allows to assess the shift problem
• Results represent simulated hours!
Configuration Sequential GVT=1 GVT=2 GVT=3 GVT=4 GVT=5
16 RobotsMean 211.86 216.31 218.27 218.69 234.99 221.81
Std. Dev. 1,56 15.11 13.28 11.07 12.46 15.64
128 RobotsMean 26.56 27.37 28.41 28.29 32.61 29.24
Std. Dev. 0.16 1,08 1,37 3,25 1.83 1.01
• Optimistic Simulation results are just upper bounds!
9 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Experimental Results: Simulation Completion Time
• Results averaged over 20 runs with different initial random seeds
• The simulation completion time allows to assess the shift problem
• Results represent simulated hours!
Configuration Sequential GVT=1 GVT=2 GVT=3 GVT=4 GVT=5
16 RobotsMean 211.86 216.31 218.27 218.69 234.99 221.81
Std. Dev. 1,56 15.11 13.28 11.07 12.46 15.64
128 RobotsMean 26.56 27.37 28.41 28.29 32.61 29.24
Std. Dev. 0.16 1,08 1,37 3,25 1.83 1.01
• Optimistic Simulation results are just upper bounds!
9 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Experimental Results: Executed Events
3.8e+07
4e+07
4.2e+07
4.4e+07
4.6e+07
4.8e+07
5e+07
5.2e+07
5.4e+07
8 16 32 64 128 256 512 1024
Tot
al (
Com
mitt
ed)
Exe
cute
d E
vent
s
Model Size (Number of Ant Robots)
Executed Events
SerialGVT=1GVT=2GVT=3GVT=4GVT=5
10 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Experimental Results: Execution Time
0
100
200
300
400
500
8 16 32 64 128 256 512 1024
Tot
al E
xecu
tion
Tim
e
Model Size (Number of Ant Robots)
Execution Time
SerialGVT=1GVT=2GVT=3GVT=4GVT=5
11 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Thanks for your attention
Questions?
pellegrini@dis.uniroma1.it
http://www.dis.uniroma1.it/∼pellegrini
http://www.dis.uniroma1.it/∼ROOT-Sim
http://www.dis.uniroma1.it/∼ROOT-Sim/tcar.tbz
12 of 12 - A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
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