GRASS GIS Raster Parallelization with OpenMP (Google Summer of Code 2021)

· 586 words · 3 minute read

Abstract 🔗

The goal of this project is to introduce parallelization to existing raster modules in GRASS GIS using OpenMP. This will allow users to take advantage of more cores in their hardware to speed up the computation time especially for large raster files with large computation cost. The key challenge of this project is to separate the parallelizable components from the sequential part of the modules without introducing too much overhead in terms of memory, disk or computation resources.

Milestones 🔗

In total, I have introduced OpenMP support to 8 raster modules in GRASS GIS. The pull requests to each module is as follows:

Firstly, I have greatly underestimated the complexity of the work. Up to 20 modules were initially proposed at first but after the second week. However, it became clear that we had to cut down on the number of target modules and focus more on designing the algorithms. The modules we targeted behave differently as compared to some modules that had received OpenMP support in the past such as r.sun. In particular, the modules need to keep the same of behavior of having low memory footprint even after the parallelization, unlike r.sun which loads the entire raster map in-memory.

During the first half of the GSoC, with the mentors’ discussion, we have came out with three different approaches to introducing parallel support to r.neighbors. After benchmarking their performance and taking account of their memory/disk usage, we decided to settle with the last approach which requires us to add an extra parameter memory to allow users to adjust their memory consumption. With this approach, we have to allow the modules process the raster map by chunks. Once we settled about the design, we started applying the same approach to other similar modules with low memory footprints. For more information regarding the implementation, see Raster Parallelization with OpenMP.

Furthermore, test scripts were included in the modules to ensure the consistency of the results. Benchmark scripts were added to allow users to easily benchmark the performance of the parallelization to monitor the speedup in their own local machine. User documentation were also modified to include sections detailing how to make use of the newly added features.

Future work 🔗

In the future, more raster modules can be parallelized using similar approach. Then, we can consider tackling more complex modules such as r.watershed and r.mapcalc. Also, we could consider exploring 3D raster modules as well.

Furthermore, when we implement parallelization for r.univar, we notice that modules that produce statistics involving arithmetics can often have floating point discrepancies when dealing with large summation. Because of this, computation using different number of threads will now produce different results due to having different order of arithmetics. One idea would be to introduce Kahan summation algorithm to reduce the floating point discrepancies. However, this still would not guarantee the consistency of results.

Relevant links 🔗

For the project overview, please visit the Project Dashboard.

For the project timeline and logs, please visit the Project Wiki.

Please visit the project repository for more details.

Acknowledgement 🔗

I would like to thank Huidae Cho, Vaclav Petras and Māris Nartišs for their guidance and support. Also, I would like to give my thanks to Anna Petrasova for helping out with the benchmarking process. Lastly, I would also like to thank the GRASS community and GSoC team for providing us such a memorable GSoC journey.

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