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Cyberinfrastructure for Scalable and High Performance Geospatial Computation Xuan Shi Graduate assistants supported by the CyberGIS grant Fei Ye (2011) and Zhong Chen (2012) School of Computational Science and Engineering (CSE) College of Computing, Georgia Institute of Technology

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Overview Keeneland and Kraken: the Cyberinfrastructure for our research and development Scalable and high performance geospatial software modules developed in the past 1 year and 7 months

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Keeneland: a hybrid computer architecture and system A five-year Track 2D cooperative agreement awarded by the National Science Foundation (NSF) in 2009 Developed by GA Tech, UT-Knoxville, and ORNL 120 nodes [240 CPUs + 360 GPUs] Integrated into XSEDE in July 2012 Blue Waters full scale of hybrid computer systems

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Kraken: a Cray XT5 supercomputer As of November 2010, Kraken is the 8th fastest computer in the world The worlds first academic supercomputer to enter the petascale Peak performance of 1.17 PetaFLOPs 112,896 computing cores (18,816 2.6 GHz six-core AMD Opteron processors) 147 TB of memory

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Scalable and high performance geospatial computation (1) Scalable and high performance geospatial computation (1) Data Size Time and Speedup on desktop Time and Speedup on Keeneland Single CPU Single GPU 1 GPU 3 GPUs 6 GPUs 9 GPUs 2191 1331 (22.2) 15.3 / 87 3 / 444 4 / 333 6 / 222 4596 2502 (41.7) 14.6 / 171 5 / 500 7 / 357 8 / 313 5822 2926 (48.8) 16.5 / 177 7 / 418 5 / 585 6 / 488 6941 3717 (62.0) 17.1 / 217 6 / 620 4 / 929 7 / 531 6 / 620 7689 3978 (66.3) 18.4 / 216 7 / 568 5 / 796 6 / 663 8 / 497 9543 4875 (81.3) 20.6 / 237 7 / 696 4 / 1219 6 / 813 8 / 609 9817 5061 (84.4) 21.2 / 239 7 / 723 4 / 1265 6 / 844 7 / 723 Performance comparison based on different scale of data (i.e. number of sample points) and the computing resources (Time is counted in second) Speedup is calculated by the time used on a single CPU divided by the time used on the GPU(s) Interpolation is calculated based on the value of 12 nearest neighbors Output grid size: 1M+ cells Interpolation Using IDW Algorithm on GPU and Keeneland

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Data Size Time/speedup on desktop Time/Speedup on Keeneland Single CPU Single GPU 1 GPU 3 GPUs 6 GPUs 9 GPUs 2191 669 (11.2) 56 / 12 7 / 96 4 / 167 6 / 112 4596 1570 (26.2) 66 / 24 8 / 196 5 / 314 6 / 262 7 / 224 6941 1960 (32.7) 65 / 30 7 / 280 4 / 490 7 / 280 6 / 327 9817 2771 (46.2) 52 / 53 6 / 462 4 / 693 7 / 396 6 / 462 Scalable and high performance geospatial computation (2) Scalable and high performance geospatial computation (2) Performance comparison based on different scale of data (i.e. number of sample points) and the computing resources (Time is counted in second) Speedup is calculated by the time used on a single CPU divided by the time used on the GPU(s) Interpolation is calculated based on the value of 10 nearest neighbors Output grid size: 1M+ cells Interpolation Using Kriging Algorithm on GPU and Keeneland Three Kriging approaches a) Spherical, b) Exponential, and c) Gaussian have been implemented on GPU/Keeneland

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Scalable and High Performance Geospatial Computation (3) Parallelizing Cellular Automata (CA) on GPU and Keeneland (1) Cellular Automata (CA) is the foundation for geospatial modeling and simulation, such as SLEUTH for urban growth simulation Game of Life (GOL), invented by Cambridge mathematician John Conway, is a well-known generic CA that consists of a collection of cells which, based on a few mathematical rules, can live, die or multiply. The Rules: For a space that is 'populated': Each cell with one or no neighbors dies, as if by loneliness. Each cell with four or more neighbors dies, as if by overpopulation. Each cell with two or three neighbors survives. For a space that is 'empty' or 'unpopulated' Each cell with three neighbors becomes populated.

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Scalable and High Performance Geospatial Computation (3) Parallelizing Cellular Automata on GPU and Keeneland (2) Size of CA: 10,000 x 10,000 Number of iterations: 100 CPU time: ~ 100 minutes GPU [desktop] time: ~ 6 minutes Keeneland [20 GPUs]: 20 seconds CPU Intel Xeon CPU 5110 @ 1.60 GHz, 3.25 GB of RAM GPU NVIDIA GeForce GTX 260 with 27 streaming multiprocessors (SM) A cell is born if it has exactly 3 neighbors, stays alive if it has 2 or 3 living neighbors, and dies otherwise. A simple SLEUTH model has implemented on a single GPU Implementation on Kraken and Keeneland using multiple GPUs is under development

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Scalable and High Performance Geospatial Computation (4) Parallelizing ISODATA for Unsupervised Image Classification on Kraken (1) Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) Performance comparison :: ERDAS uses 3:44:37 (13,477 seconds) to read image file [~ 2 minutes] and do the classification over one tile of 18 GB imagery data [0.5 m resolution in three bands] Number of Cores 144324576900 Stripe Count 80808080 Stripe Size (MB) 10101010 Read Time (Sec) 5.665.132.942.77 Classification Time (Sec) 13.726.153.563.31 Our solution over Kraken using different number of cores with optimized stripe count and stripe size. Tue Jun 12 12:48:37 EDT 2012 Iteration 1: convergence = 0.000 Iteration 2: convergence = 0.918 Iteration 3: convergence = 0.938 Iteration 4: convergence = 0.954 ---- Classification completed ---- The reading file time is 15.4807 The classification time is 9.2374 The total ISODATA algorithm running time is 24.7181 Histogram: Class 0: 1124674113 Class 1: 1970406180 Class 2: 2845484626 Class 3: 2897947070 Class 4: 2298948648 Class 5: 1662539363 Application 1436660 resources: utime ~30211s, stime ~1215s Tue Jun 12 12:49:06 EDT 2012 Tue Jun 12 15:39:10 EDT 2012 Iteration 1: convergence = 0.000 Iteration 2: convergence = 0.919 Iteration 3: convergence = 0.936 Iteration 4: convergence = 0.953 ---- Classification completed ---- The reading file time is 53.5952 The classification time is 9.1167 The total ISODATA algorithm running time is 62.7119 Histogram: Class 0: 2811537615 Class 1: 8743937711 Class 2: 12122628756 Class 3: 11850984345 Class 4: 9714452352 Class 5: 5956459221 Application 1440071 resources: utime ~208415s, stime ~4110s Tue Jun 12 15:40:18 EDT 2012 Tue Jun 12 14:24:23 EDT 2012 Iteration 1: convergence = 0.000 Iteration 2: convergence = 0.915 Iteration 3: convergence = 0.935 Iteration 4: convergence = 0.952 ---- Classification completed ---- The reading file time is 28.6973 The classification time is 8.9810 The total ISODATA algorithm running time is 37.6782 Histogram: Class 0: 2811537615 Class 1: 3715199078 Class 2: 5660559329 Class 3: 5766104126 Class 4: 4652035362 Class 5: 2994564490 Application 1439048 resources: utime ~78392s, stime ~2164s Tue Jun 12 14:25:05 EDT 2012 Tue Jun 12 16:06:31 EDT 2012 Iteration 1: convergence = 0.000 Iteration 2: convergence = 0.919 Iteration 3: convergence = 0.937 Iteration 4: convergence = 0.953 ---- Classification completed ---- The reading file time is 47.8197 The classification time is 9.6519 The total ISODATA algorithm running time is 57.4716 Histogram: Class 0: 2811537623 Class 1: 14137169249 Class 2: 18231156326 Class 3: 17844190199 Class 4: 14839032207 Class 5: 8936914396 Application 1440335 resources: utime ~275810s, stime ~6377s Tue Jun 12 16:07:33 EDT 2012 36 GB 72 GB 144 GB 216 GB 1,800 Cores 3,600 Cores 7,200 Cores 10,800 Cores 20+ hours to load data from GT into Kraken @ ORNL The more cores are requested, the longer the waiting time will be ~ 10 seconds to complete the classification process I/O needs to be further optimized

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# of tiles 5 classes 10 classes 15 classes 20 classes I/OCLSTotalIRI/OCLSTotalIRI/OCLSTotalIRI/OCLSTotalIR 14.322.136.4544.258.6212.87115.5112.0717.57116.0018.1324.1313 28.942.1611.10420.317.9228.231017.1611.3228.47119.0215.0924.1112 421.012.2123.23416.407.9524.351014.8013.4128.211316.407.9524.3510 828.832.2331.06428.957.4136.36928.6714.7843.461429.5215.3444.8612 1244.862.2947.15445.926.5752.49858.319.4367.74941.5615.3756.9312 Scalable and High Performance Geospatial Computation (4) Parallelizing ISODATA for Unsupervised Image Classification on Kraken (2) Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) Performance comparison to classify one tile of 18 GB image into 10, 15, and 20 classes, ERDAS uses about 5.5, 6.5, and 7.5 hours to complete 20 iterations, while the convergence number is less than 0.95

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Through a re-engineering process, the near-repeat calculation is first parallelized on to a NVIDIA GeForce GTX 260 GPU, which takes about 48.5 minutes to complete one calculation and 999 simulations on two event chains over 30,000 events. Through a combination of MPI and GPU programs, we can dispatch the simulation work onto multiple nodes in Keeneland to accelerate the simulation process. We use 100 GPUs on Keeneland to implement 1,000 simulations for about 264 seconds to complete this task. If more GPUs were used, the simulation time can be reduced. Scalable and High Performance Geospatial Computation (5) Near-repeat calculation for spatial-temporal analysis on crime events over GPU and Keeneland One run of 4+ event chain calculation is easy to approach or go beyond petascale (10 15 ) and exascale (10 18 )

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Thank you Questions?