QGIS plugin for parallel processing in terrain analysis
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This document discusses the development of a QGIS plugin for parallel processing in terrain analysis, created as part of an NSF REU project to mentor undergraduates in computer science research. The plugin leverages GPU capabilities to significantly speed up terrain analysis tasks compared to traditional methods, achieving faster computation times by utilizing parallel processing techniques. Future steps include improving the installation process and expanding compatibility with Windows.
QGIS plugin for parallel processing in terrain analysis
- 1. © Arthur J. Lembo, Jr. Salisbury University QGIS Plug-in for Parallel Processing in Terrain Analysis' Arthur Lembo Department of Geography and Geoscience @artlembo
- 2. © Arthur J. Lembo, Jr. Salisbury University If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens? - Seymour Cray
- 3. © Arthur J. Lembo, Jr. Salisbury University • As part of a NSF REU, we built a QGIS plugin to perform terrain-based parallel processing with Python. • This presentation shows the results of our undergraduate student research project – Multicore processors – Massively parallel GPGPUs – Hardware evolution – Our QGIS plug-in – The road ahead Overview
- 4. © Arthur J. Lembo, Jr. Salisbury University NSF REU • The NSF REU is a 3 year grant (extended to 5 years) focused on parallel processing. • The goal is to expose undergraduates to academic research in computer science. • My role has been to mentor students in the use of parallel processing in geography.
- 5. © Arthur J. Lembo, Jr. Salisbury University • 1971 Intel 4004 • Ted Hoff • $60,000 • 2,300 transistors • 582,000,000 Quad • Lithography • Killed time-sharing Microcomputer revolution
- 6. © Arthur J. Lembo, Jr. Salisbury University • 2x / 18 months • Design Shrink • Smaller = Faster Moore’s Law
- 7. © Arthur J. Lembo, Jr. Salisbury University Trouble in paradise?
- 8. © Arthur J. Lembo, Jr. Salisbury University • Heat – Limits on power density – Package dissipation limit – Watercooled overclocking • Subunit Complexity – Single clock cycle synchronicity – AMD translation lookaside buffer bug • RC Interconnect delay Limits of Moore’s Law
- 9. © Arthur J. Lembo, Jr. Salisbury University • Parallel dies • Parallel packages • Core 2 Duo • Core 2 Quad Repeating history Parallel microprocessors
- 10. © Arthur J. Lembo, Jr. Salisbury University • 64-bit just getting traction • Windows Parallelism • Multithreading difficult to Code • Parallel code even harder • Scientific Computing • Who care’s what I have to say? • Gaming leads the way Limited uptake Opening week: Grand Theft Auto: $500M Halo 3: $300M Spiderman 3: $182M Pirates 3: $196M
- 11. © Arthur J. Lembo, Jr. Salisbury University
- 12. © Arthur J. Lembo, Jr. Salisbury University
- 13. © Arthur J. Lembo, Jr. Salisbury University Options for Large Geographic Computations • Use a smaller dataset • Generalize the resolution of your dataset – Both options compromise the integrity of the data • Invest in clusters (groups of ordinary PCs joined together with combined power and parallel processing) or time sharing – Require special programming – Very costly
- 14. © Arthur J. Lembo, Jr. Salisbury University Background Parallel Processing - program allows multiple computations to occur concurrently GPU - graphical processing unit, generally used for video / gaming visuals processing Designed for multithreading, contain hundreds of cores, good at simple math CPU - central processing unit, what computation is traditionally done on Contain much smaller number of cores, good at complex calculations
- 15. © Arthur J. Lembo, Jr. Salisbury University Test Environment GPU - Nvidia GTX 670 1344 CUDA cores 2 GB DDR5 RAM Intel Xeon E5607 processor 4 Cores, 4 threads 2.27 GHz
- 16. © Arthur J. Lembo, Jr. Salisbury University Why PyCUDA • Expose CUDA functions in QGIS • Easier to program? • Easier to add functionality?
- 17. © Arthur J. Lembo, Jr. Salisbury University Terrain functions • Started with 3 common GIS functions • Slope - ~15 calculations, Aspect - ~20 calculations, Hillshade - ~45 calculations • All are embarrassingly parallel
- 18. © Arthur J. Lembo, Jr. Salisbury University Terrain visuals Altitude HillshadeSlope
- 19. © Arthur J. Lembo, Jr. Salisbury University Methods, cont.
- 20. © Arthur J. Lembo, Jr. Salisbury University Scheduler • Overall manager • Starts and manages processes which load data, • Performs raster calculations on GPU, • Save data back to disk.
- 21. © Arthur J. Lembo, Jr. Salisbury University GPU Calculator • Where the actual calculations are performed • Reads data from the input pipe, performs GPU calculations over all the cores, sends result through output pipe to data saver • Designed so any algorithm based on a 3x3 grid of pixels can be used
- 22. © Arthur J. Lembo, Jr. Salisbury University Saver • Takes the results of the calculations done in the GPU manager and saves them to disk • Uses the GDAL libraries to write multiple lines at a time to a Geotiff • Multiple savers can all run in parallel to save the ouputs of different functions
- 23. © Arthur J. Lembo, Jr. Salisbury University Python by itself is much slower than C++ PyCUDA is faster both because of utilizing the GPU and because it is written in C *take away: when given the option, use pyCUDA libraries Results and Discussion (Stage 1 - out of the box) Size Python C++ PyCUDA QGIS 25 MB 50 secs 5 secs 4 secs 5 secs 200 MB 7:30 mins 40 secs 28 secs 15 secs
- 24. © Arthur J. Lembo, Jr. Salisbury University Results and Discussion (Stage 2 - threading) Adding CPU based parallelism increases gains Reduces time waiting for data to be given to GPU Size Threaded Python Threaded C++ Threaded PyCUDA QGIS 25 MB 45 secs 5 secs 4 secs 5 secs 200 MB 7:30 mins 40 secs 9 secs 15 secs 1.5 GB 1:30 hrs 18:03 mins 9:04 mins 9 mins
- 25. © Arthur J. Lembo, Jr. Salisbury University Results and Discussion (Stage 2 - added computation) Adding more complex computations allows us to maximize the GPU contribution. Computing hillshade which requires about 3x more computations PyCUDA doesn’t even slow down when switching formulas Shows that it can do much more before peaking out Size QGIS Threaded PyCUDA 25 MB 5 secs 4 secs 1.5 GB 11:00 mins 9:04 mins 12 GB 45:00 mins 50:00 mins
- 26. © Arthur J. Lembo, Jr. Salisbury University Results and Discussion (Stage 3 - further optimization) Main bottleneck in computations disk I/O Total time the GPU is working for the 1.5 GB file is less than 2 seconds Increasing size of reads and writes gains even more time Size QGIS Threaded PyCUDA Input 2:00 mins 1:55 mins Computation 9:00 mins 1:00 mins Output 2:00 mins 2:20 mins Total 11:00 mins 3:35 mins
- 27. © Arthur J. Lembo, Jr. Salisbury University Results and Discussion (Stage 3 - chunking) Lines read Time taken 1 50:00 10 39:50 15 28:30 20 33:30 30 37:00 40 40:00 50 48:30 Lines read Time taken 1 5:18 10 3:54 15 3:00 20 3:50 30 4:16 40 4:52 50 5:21 Reading too much data in one call causes slowdown Optimal number is ~15 lines for all sizes No apparent ratio between disk read lines and raster column and row sizes 1.5 GB is 14400 rows * 28800 cols 12 GB is 51187 rows * 60818 cols The limitation is how fast we can send data to GPU 12 GB file 1.5 GB file
- 28. © Arthur J. Lembo, Jr. Salisbury University Results • The PyCUDA version is consistently faster than QGIS when calculating hillshade for files of various sizes. • GPU computations, including CPU based memory management took one ninth of the time required to do the same thing in QGIS • The I/O bottleneck can be seen in the input and output sections of the second table. • Output takes a much longer time because it has to wait for the GPU to pass data to the saver before it can start saving to disk 9:00
- 29. © Arthur J. Lembo, Jr. Salisbury University Other Takeaways • CUDA is very efficient when you have a smaller number of data elements, but massive calculations per element. • Terrain based analysis use massive amounts of data, but few calculations per data element.
- 30. © Arthur J. Lembo, Jr. Salisbury University Earlier work
- 31. © Arthur J. Lembo, Jr. Salisbury University Next Steps • Improve the installation process – it is too arduous at the moment • Get the plug-in to work in Windows
- 32. © Arthur J. Lembo, Jr. Salisbury University Conclusion • Early results show the ability to triple terrain analysis speed compared to serial methods • Multithreading can significantly improve GIS analysis speed • Try it out for yourself: https://github.com/aFuerst/PyCUDA-Raster GPU C++ QGIS SERIAL
- 33. © Arthur J. Lembo, Jr. Salisbury University SO, WHAT IS A GOOD GIS EXAMPLE OF MASSIVE CALCULATIONS PER DATA ELEMENT?
- 34. © Arthur J. Lembo, Jr. Salisbury University Acknowledgements Salisbury University National Science Foundation (Award # 1460900) Students: William Hoffman Charlie Kazer Alex Fuerst