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Opportunities for SLEUTH in High-performance Computing

Opportunities for SLEUTH in High-performance Computing. 2102 AAG Annual Meeting New York, NY. Qingfeng (Gene) Guan, Ph.D. Center for Advanced Land Management Information Technologies School of Natural Resources University of Nebraska - Lincoln. Introduction

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Opportunities for SLEUTH in High-performance Computing

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  1. Opportunities for SLEUTH in High-performance Computing 2102 AAG Annual Meeting New York, NY Qingfeng (Gene) Guan, Ph.D. Center for Advanced Land Management Information Technologies School of Natural Resources University of Nebraska - Lincoln

  2. Introduction pRPL– a general-purpose Raster Processing programming Library pSLEUTH– parallelizing SLEUTH using pRPL Parallelizing CA using GPUs Conclusions Content

  3. The urban growth model SLEUTH, uses a modified Cellular Automatato model the spread of urbanization across a landscape (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. (Goldstein. 2004) Introduction - SLEUTH Prediction of urban development to the year 2050 over southeastern Pennsylvania and part of Delaware using the SLEUTH model http://www.essc.psu.edu/~dajr/chester/animation/movie_small.htm

  4. Coefficients Dispersion Breed Spread Slope Road Gravity Introduction - SLEUTH • Rules • Spontaneous Growth Rule (centralized) • New Spreading Centers Rule (non-centralized) • Edge Growth Rule (non-centralized) • Road-Influenced Growth Rule (non-centralized)  For more info. about SLEUTH: http://www.ncgia.ucsb.edu/projects/gig/

  5. Bruce Force Calibration 1015 coefficient combinations Monte Carlo Iteration 10 ~ 100 Iterations for each coefficient combination Massive-volume Data High spatial resolution Large study area High Computational Intensity “The model calibration for a medium sized data set and minimal data layers requires about 1200 CPU hours on a typical workstation” (Clarke. 2003) Introduction - SLEUTH Calibration of SLUETH http://www.ncgia.ucsb.edu/projects/gig/

  6. Solution 1: Simplifying Assumptions Each coefficient affects the model behavior in a linear manner Iteratively narrow down the coefficient search range 0, 50, 100 50, 75, 100 50, 62, 75 … Reach the “optimal” coefficient value Problem: random processes in CA  very likely non-linear relationships  assumptions generate unreliable calibration results  inaccurate simulation results possibly flawed conclusions and theories, and improper management and planning decisions Introduction- SLEUTH

  7. Introduction - SLEUTH • Solution 2: “Smart” Agents – Computational Intelligence • Genetic algorithm, Artificial Neural Networks, Ant Intelligence, etc. • Search the coefficient space more efficiently • Issue: which one is better/smarter? • Solution 3: High-performance Computing (HPC) • “Dumb Dumb with Fast Legs” • Exhaust the coefficient space in a acceptable/feasibletime period • Remove simplifying assumptions

  8. Introduction – High-performance Computing (HPC) • Parallel computing is the use of multiple computing units (computers, processors, or processes) working together on a common task in a concurrent manner in order to achieve higher performance • In contrast to sequential computing that usually has only one computing unit • Performance is usually measured with computing time A massive parallel computing system (http://ctbp.ucsd.edu/pc/html/intro4.html )

  9. Raster is born to be parallelized pRPL: parallel Raster Processing Library

  10. Writing a parallel raster processing program is as simple as writing a serial program Possible usage Massive-volume geographic raster processing Image (including remote sensing imagery) processing Large-scale Cellular Automata (CA) and Agent-based Modeling Free downloadable and open source http://sourceforge.net/projects/prpl/ pRPL- Introduction

  11. Object-Oriented programming style Written in C++ Built upon the Message Passing Interface (MPI) Class templates support arbitrary data types e.g. integer, char, double precision floating point number, even user-defined types Transparent Parallelism pRPL- Features

  12. Spatially Flexible Supports any arbitrary neighborhood configuration Supports centralized and non-centralized algorithms pRPL- Features (cont.)

  13. Regular and irregular decomposition pRPL- Features (cont.)

  14. Multi-layer processing pRPL- Features (cont.)

  15. The four growth rules in the SLEUTH model, were implemented using pRPL pSLEUTH- Parallelization

  16. Processer Grouping With pRPL, pSLEUTH is able to organize the processors in groups. Data parallelism within a group, task parallelism among groups. Static tasking and dynamic tasking pSLEUTH- Parallelization (cont.)

  17. Data Urban areas of the continental US (1980 and 1990, 4948×3108) Calibration Settings Only three values (0, 50, 100) will be evaluated for each coefficient The total number of simulations is 243 (= 35) Each simulation includes 11 ( = 1990-1980+1) years pSLEUTH- Experiments Settings

  18. pSLEUTH - Experiments Settings • Computer cluster at University of California, Santa Barbara • 128 dual CPU 3.06 GHz Xeon nodes • 2-GB RAM each

  19. pSLEUTH – Performance

  20. GPU- Introduction • Graphics Processing Unit (GPU) • Massive number (hundreds) of computing cores on one unit • Very inexpensive ($200 - $2000) • General parallel processing with Compute Unified Device Architecture (CUDA) • Personal Supercomputer

  21. GPU – Preliminary Study • Classical CA - Game Of Life (GOL) • 10,000 X 10,000 • 100 iterations • Desktop PC with 1.6 GHz dual-core CPU • 100 minutes • Desktop PC with NVIDIA GeForce GTX 260 GPU • 192 cores • 6 minutes • 16.6 speed-up

  22. GPU – Preliminary Study • Keeneland • Hybrid computer cluster with multiple CPUs and GPUs • 120 nodes with 240 CPUs and 360 GPUs • Developed by Georgia Institute of Technology, University of Tennessee at Knoxville, and Oak Ridge National Laboratory, sponsored by NSF • Keeneland-GOL • 8 GPUs used, 25 seconds, 240 speed-up • 20 GPUs used, 20 seconds, 300 speed-up

  23. HPC greatly reduces the computing time for model calibration Enables the removal of simplifying assumptions Can increase the reliability of calibration and accuracy of simulation Conclusion • New computing accelerator technology enables HPC-SLUETH with very low costs • Opportunities for us to test and verify conclusions and theories, as well as to stimulate new theories

  24. Comments and Questions?

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