1. Parallel Computing for Urban Cellular Automata Qingfeng. Gene. Guan 2004-Nov-18 Geography Department Colloquium Univ. of California, Santa Barbara

2. Cellular Automata (CA) A classical CA is a set of identical elements, called cells, each one of which is located in a regular, discrete space. Each cell can be associated with a state from a finite set. The model evolves in discrete time steps, changing the states of all its cells according to a transition rule, homogeneously applied at every step. The new state of a certain cell depends on the previous states of a set of cells, which can include the cell itself, and constitutes its neighborhood.

3. Components & Boundary Terms of CA Cells States Neighborhood Transition Rules Space Time

4. An Example of 2D CA = 1 = 0 if {Sum[State(Neighbor(i))] >= 1 & State(i) = 0} then State(i) = 1

5. An Example of 2D CA (cont.)    t = 0 t = 1 t = 2 t = 3 t = 4

6. Want to See More? ^_^ Here we go …  

7. CAs in Geographic Research Land Cover/Use Change Simulation –Urban Growth Wild Fire Simulation Flood, Lava & Desert Spread Simulation Traffic Flow Simulation More and More Coming up… Anthony Gar-On Yeh, Xia Li. 2003

8. Example: Online Traffic Flow Simulation R. Barlovic et al. Online Traffic Simulation with Cellular Automata. 1999

9. SLEUTH @ UCSB The urban growth model SLEUTH, uses a modified CA to model the spread of urbanization across a landscape (Keith C. Clarke et al., 1996, 1997). Its name comes from the GIS data layers that are incorporated into the model; Slope, Landuse, Exclusion layer (where growth cannot occur, like the ocean), Urban, Transportation, and Hillshade. (Noah C. Goldstein. 2004)

10. CA in the SLEUTH Coefficients –Dispersion –Breed –Spread –Slope –Road Gravity Rules –Spontaneous Growth Rule –New Spreading Centers Rule –Edge Growth Rule –Road-Influenced Growth Rule  For more info. about SLEUTH: http://www.ncgia.ucsb.edu/projects/gig/

11. Why Parallel? Data Intensity –GIS users today have access to an unprecedented amount of high resolution and high-quality data through scanners, remote sensing devices, GPS receivers, government agencies, social organizations, commercial companies, etc. –Example: Cell size 50 X 50 m. Cell space 300 X 300 km. Then the CA needs to process 6000 X 6000 cells. What about a space of 1000 X 1000 km? 4 X 10 8 Cells!! What if higher resolution and bigger space?...

12. Why Parallel? (cont.) Computational Intensity –More Data  More Computation –More Complicated Rules  More Computation SLEUTH: 5 coefficients, 4 rules, self-modification –Coefficient Calibration  More Computation SLEUTH: 5 coefficients, Range of [0 100], 101 5 coefficient sets –Monte Carlo Iteration  More Computation SLEUTH: 10 ~ 100 Iterations for each coefficient set are suggested –Other Factors…

13. Why Parallel? (cont.) “The model calibration for a medium sized data set and minimal data layers requires about 1200 CPU hours on a typical workstation” (Keith C. Clarke. 2003). High-performance computing is required Solution: Parallelization Goal: To process more data, more coefficient sets, more Monte Carlo iterations, in less time

14. Strategies for Parallelization (1) Data-oriented Parallelization –Split the whole dataset into sub datasets and assign them to multiple processors; these processors deal with sub datasets in parallel –CA was born to be parallelized with this strategy: Split the whole cell space into sub cell spaces –Solution for computation intensity from huge cell space

15. Strategies for Parallelization (2) Task-oriented Parallelization –Split the whole task into sub tasks and assign them to multiple processors; these processors perform sub tasks in parallel –For the SLEUTH, coefficient set evaluation and Monte Carlo iterations can be parallelized with this strategy –Solution for computation intensity from complex rules

16. Strategies for Parallelization (3) Combination of the previous two strategies –Each processor perform evaluation of a certain coefficient set or a Monte Carlo iteration on a certain sub cell space

17. Challenge (1) Communication Overhead –Definition: Information flow among processors –Challenge: How to minimize it? How to make the massage passing more efficient? –Possible Solution: Ghost Cells

18. Challenge (2) Load Balance –Definition: Balance of Data & Task load among processors –Challenge: How to deal with sparse grid? –Possible Solution: Irregular Parsing

19. Research Questions Which parallelization strategy is best for CA model use for calibration, and for forecasting? –Depends on the data amount, CA model, task dependence, software & hardware, etc Can multiple parallelization approaches be implemented and compared? Does Ghost Cells method work for geographic CA (e.g. Urban CA)? How sparse can a grid be and still benefit from data parsing?

20. Future Work Dependence Analysis of the SLEUTH Data & Task Parsing Methods for the SLEUTH Communication Optimizing Methods Load Balance Optimizing Methods Implementation & Comparison

21. Further Future Supercomputer / Cluster? Peer-to-Peer Computing? –SLEUTH@Home Grid Computing? –GRID SLEUTH

22. Acknowledgement Prof. Keith Clarke, Dept. of Geography, UCSB Noah Goldstein, Dept. of Geography, UCSB Charles Dietzel, Dept. of Geography, UCSB Jeff Hemphill, Dept. of Geography, UCSB

23. Comments & Questions?