1. Agent-Based Computing CSS599 Winter 2018
Munehiro Fukuda, Ph.D.
Computing & Software Systems University of Washington Bothell
January 24, 2017
CSS599 Talk

2. Outline Software agents Research Topic 1 Research Topic 2
Agent-Based Models
Research Topic 2
Agent-Based Big Data Analysis
Student Projects
Past students achievements
Current student projects
Expected activities
January 24, 2017
CSS599 Talk

3. Software Agents Cognitive Agents Reactive Agents Our focus
coarse-grain execution entities that achieve network-administrative and computation-intensive task, based on their behavioral intelligence
fine-grain entities, each reacting to its environment with simple rules.
Our focus
January 24, 2017
CSS599 Talk

4. Topic 1 Agent-Based Simulation
January 24, 2017
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5. Macro- versus Micro-Simulation Top-down versus Bottom-up Approach
Macroscopic/Top-down
Microscopic/Bottom-up
Mathematical models
Schroedinger eq. (for wave)
Euler eq. (for fluid dynamics)
Easy verification
Mathematical verification
Easy access to public data (e.g. traffic flow)
Inaccurate enough
No support of object-interaction
Difficulty in parallelization
Space partition/mapping etc.
Focus on an emergent, collective group behavior of many objects
Molecular level
Pedestrian/vehicle level
Difficulty verification
Calibration required
Fewer real data available to the public (e.g. congestion at a traffic light)
Scalable computation required
Easy parallelization
No partition required (yet object-CPU mapping needed)
January 24, 2017
CSS599 Talk

6. Molecular Dynamics Conventional Top-down Approach
for ( int i = 0; i < N; i++ ) for ( int j = 0; j < I; j++ ) if ( sqrt( pow( x[i] – x[j], 2.0 ) + pow( y[i] – y[j], 2.0 ) ) < forceCutoff ) { ax[i] += fOver2 * ( x[i] – x[j] ); ax[j] -= fOver2 * ( x[i] – x[j] ); ay[i] += fOver2 * ( y[i] – y[j] ); ay[i] += fOver2 * ( y[i] – y[j] ); }
i = molecular_range_begin[rank]; i < molecular_range_end[rank]; i++ )
MPI_Allreduce( ax, tmp_ax, N, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
MPI_Allreduce( ay, tmp_ay, N, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
computer 0
computer 1
Computer 2
January 24, 2017
CSS599 Talk

7. Molecular Dynamics Agent-based bottom-up Approach
Each agent:
compute( int t ) {
for ( Agent neighbor : place.agents ){
my_ax += fOver2 * ( my.x – neighbor.x );
my_ay += fOver2 * ( my.y – neighbor.y ); }
Agent-Based Computing Middleware
computer 0
computer 1
Computer 2
January 24, 2017
CSS599 Talk

8. Multi-Agent Spatial Simulation
January 24, 2017
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9. MASS Specification January 24, 2017 CSS599 Talk
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( )
January 24, 2017
CSS599 Talk

10. Implementation Status
The latest status is given at:
MASS Java
Intended for agent-based big data.
MASS C++
Intended for agent-based models.
MASS GPU
In progress
Debugger
Available for MASS Java and C++ versions
Availability
Pilot used in CSS534 summer 2012, winter 2015, and autumn 2016
Java and C++ versions arealdy released to publicly
January 24, 2017
CSS599 Talk

11. Agent-Based Models in Other Desciplines
Biology/Ecology
Immune system simulation
Epidemic simulation
Neural network simulation
City Planning
Transport simulation
Electrical grid
Social, Behavioral, and Economic Sciences
Ambient intelligence
Social network
Economic crisis simulation
start
goal
45 minutes
January 24, 2017
CSS599 Talk

12. Agent-Based Transport Simulation Parallelized by Zach Ma and presented at WSC2015
Road network converted to an adjacency list
Programmability
LoC
Files
Original MatSim
5,144 46
Code addition/modification by MASS
585 8 %
11.3%
17.4%
Execution performance
Vehicle drives converted to agent migration
January 24, 2017
CSS599 Talk

13. Collision-Free Agent Migration Presented at FedCSIS/JAWS 2017
Sequential migration
Upper-left agents have a higher priority than lower-right agents.
Parallel migration
All agents can migrate at once.
We need some prioritization.
MATSim Traffic simulation
Greedy parallel migration
Location-ordered migration
Direction-ordered migration
Sequential migration
collision
another direction
Distributed termination detection
required
5 ordered migrations
4 ordered migration direction
January 24, 2017
CSS599 Talk

14. Our Future Goals
Investigating what parallel-programming features attract scientists who are not necessarily computing specialists:
Parallelizing ABM applications of our collaborators.
Developing benchmark testsets that cover various agent behaviors.
January 24, 2017
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15. Topic 2 Agent-Based Data Analysis
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16. Topic 2: Agent-Based Big Data How different from conventional approach when counting triangles in a graph for example
MapReduce, Spark, and Storm
Agent-based Big Data
Analytics stay passive to receive data.
All data must be examined.
Data must be flattend to feed.
Analytics actively move over data.
Only necessary portion of data can be examined.
Data can maintain its structure.
Code is written from the driver’s viewpoint.
(1,2), (2,1), (2,3), (3,2), (2,4), (4,2), (2,5), (5,2), (3,4), (4,3), (4,5), (5,4)
(1, [2]), (2, [1,3,4,5]), (3, [2,4]), (4, [2,3,5]), (5, [2,4]) 1
[(1,2), -], [(1,3), 2], [(2,3), -], [(2,3), 4], [(2,4), -], [(2,4),3],
[(2,4), 5], [(2,5), 4], … 2 5
(2,3) [-,4]  (2,3,4), (2,4) [-,3,5] -> (2,4,3), (2,4,5)…
(2,3,4), (2,3,4), (2,3,4), (2,4,5), (2,4,5)..
(2,3,4), (2,4,5) 3 4
January 24, 2017
CSS599 Talk

17. Our Target Applications
Injecting a group of agents into big data
Scientific Computing
Climate analysis
Prof. Kim’s biological network motif search
Data Sciences / Machine Learning
K-means clustering
K-nearest neighbors
GIS/Spatial Data Analysis (represeted by graph problems)
Shortest path
Chinese postman problem
Triangle counting
page ranking
n1(0.1)
n4(0.2)
n5(0.383)
n3(0.183)
n2(0.133)
January 24, 2017
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18. Climate Analysis parallelized by Jason Woodring and presented at ICDCS 2017
Detect time of emergence
The biggest overhead is reading files into a cluster system
January 24, 2017
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19. Biological Network Motif Search with Prof
Biological Network Motif Search with Prof. Wooyoung Kim and Presented at HCPP 2015 and BDCloud 2016 A A A R
agent
spawn request
Agent explosion
400,000-node network with motif size 5
5.5 million agents
Needs to address
Memory allocation overheads
Agent management overheads
Controlling agent population
Don’t spawn too many agents.
In Java,
Size of each agent = 1M
Size of serialized agent = 300B R A R R R R
January 24, 2017
CSS599 Talk

20. Agent-based Clustering
Single Clustering Algorithm
Batch Clustering Algorithm
K-means
K-means + Biologically-Inspired Algorithms X X X
A Space of Potential Cluster Centroid Sets C1 X X X
agent
[c1, c2, c3] X X X X X X X X X X
swarm X X X X C2 X X X X X X X X C3 X X X X X
January 24, 2017
CSS599 Talk

21. Agent-based K-Nearest Neighbors
milliseconds X X X
# data items / place (size * size)
Sequential
MASS Java
100,000 / 1,000 * 1,000 84 92
160,000 / 4,000 * 4,000
138
282
490,000 / 7,000 * 7,000
236
589 X X X
T=5
K = 7 X
T=4 X X X X X
T=3 X X X X
T=2 ? X X X
T=1 X X X
MASS Places size:
100 * 100
400 * 400
700 * 700
So that each place may have 1 or 2 data items. X X X X X X X X X X X
Problem: An agent must be spawned by another agent but not any place.
This in turn means that we need to populate an agent at each place at the beginning,
which is a big overhead.
January 24, 2017
CSS599 Talk

22. Past Student Research Outcome (gradudate students in red and undergraduate students in blue, listed below)
Christopher Bowzer, Benjamin Phan, Kasey Cohen, Munehrio Fukuda, Collision-Free Agent Migration in Spatial Simulation, In Proc. of the Joint Agent-oriented Workshops in Synergy JAWS, Prague, Czech Republic, September 3-6, 2017
Jason Woodring, Matthew Sell, Munehiro Fukuda, Hazeline Asuncion, Eric Salathe, A Multi-Agent Parallel Approach to Analyzing Large Climate Data Sets, The 37th IEEE International Conference on Distributed Computing Systems, pages , Atlanta, GA, June 5-8, 2017
Andrew Andersen, Wooyoung Kim, and Munehiro Fukuda, MASS-based NemoProfile Construction for an Efficient Network Motif Search, 2016 Big Data and Cloud Computing – BDCloud’16, October 8-10, 2016
Zhiyuan Ma, Munehiro Fukuda, A Multi-Agent Spatial Simulation Library for Parallelizing Transport Simulation, the 2015 Winter Simulation Conference - WSC 2015, Newport Beach, December 6-9, 2015
Bhargav Mistry, Munehiro Fukuda, Dynamic Load Balancing in Multi-Agent Spatial Simulation, submitted to 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing - PACRIM’15, Victoria, BC, Canada, August 24-26, 2015 – Best Paper Award
Matthew Kipps, Wooyoung Kim, Munehiro Fukuda, Agent and Spatial Based Parallelization of Biological Network Motif Search, submitted to 17th IEEE Int’l Conference on High Performance and Communication, New York, August 24-26, 2015
Brett Yasutake, Niko Simonson, Jason Woodring, Nathan Duncan, William Pfeffer, Hazeline Asuncion, Munehiro Fukuda, Eric Salathe, Supporting Provenance in Climate Science Research, In Proc. of 7th International Conference on Information, Process, and Knowledge Management, eKnow 2015, Lisbon, Portugal, February 22-27, 2015 – Best Paper Award
Timothy Chuang, Munehiro Fukuda, A Parallel Multi-Agent Spatial Simulation Environment for Cluster Systems, In Proc. of the 16th IEEE International Conference on Computational Science and Engineering - CSE 2013, pages , Sydney, Australia, December 3-5, 2013
Somu Jabayalan, Munehiro Fukuda, Field-Based Job Dispatch and Migration, In Proc. of 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing - PACRIM'13, pages 52-57, Victoria, BC, Canada, August 27-29, 2013
January 24, 2017
CSS599 Talk

23. Thesis/Capstone Projects
Multi-Agent Simulations
Agent-Based Data Analysis
Applications
Benchmarking
MASS-C++ benchmarking (Craig Shih)
MASS-GPU benchmarking
Clustering, KNN, Graph Algorithms (Collin Gordon)
System Design
Implementation
MASS-GPU version (Lisa Kosiachenko)
CPU/GPU Hybrid Architecture
Parallel I/O
Parallel I/O (Jas Shih)
Agent Management
Programmability/Usability
Auto-description of agent behavior (using Agent-derived classes)
GUI to code generation and result visualization from simulation viewpoint
Auto-description of agent behavior (using Java annotation) (Matt Sell)
GUI to code generation and result visualization from data science viewpoint
Future projects in blue
January 24, 2017
CSS599 Talk

24. Agent-Based Clustering: Collin Gordon
Use MASS to perform clustering on data points
Variant 1 Single Clustering: Uses Agents and Places to compute one set of clusters for a given data set. Example: Call the Ball Algorithm (in progress)
Variant 2 Batch Clustering: Uses Agents and Places to compute multiple sets of clusters for a given data set. Example: Best Tool Algorithm (completed)
Currently targeting PAAMS, MAS&S, and IEEE for paper submissions.
Questions? me at
January 24, 2017
Collin Gordon

25. Code and Execution
January 24, 2017
Collin Gordon

26. MASS CUDA Lisa Kosiachenko
GPU programming model
Single Instruction Multiple Thread (SIMT) parallel programming model
GPU has many more arithmetic logical units than CPU (on NVIDIA GeForce GTX TITAN Black – 2880 cores in total)
January 24, 2017
Lisa Kosiachenko

27. MASS Current Development
Optimization of speed
Implementing new features (dynamic agents)
January 24, 2017
Lisa Kosiachenko

28. MASS CUDA Optimization: Utilizing GPU Memory Architecture
Fast (visible to a block of threads)
Very fast (visible to 1 thread)
Slow
Slow
Fast (read-only)
Fast (read-only)
January 24, 2017
Lisa Kosiachenko

29. Expected Activities Research Group Meetings
Weekly status report to your lab mates
Quarterly research presentation
Optional Individual Meetings
Weekly progress discussions, code reviews, etc. with the professor, (i.e., me).
Programming/Installation
We are a product-oriented group. You need to code something or install something useful to manage our projects
Publications
I recommend you to coauthor conference/journal papers.
January 24, 2017
CSS599 Talk

30. Questions? Please check: http://depts.washington.edu/dslab/
January 24, 2017
CSS599 Talk