Thanks Even, I think what I'm envisioning is more of an integration test than a unit test. We don't intend to use this in TorchGeo CI on every commit, only on PRs that we know may impact I/O (much less frequent than in GDAL). We would also run it before each release and publish performance metrics to prevent regressions. Since it's run infrequently and manually, we wouldn't suffer from the same issues of 20% buffering and could actually run multiple times and average.
For TorchGeo, we definitely want to consider full-sized tiles/scenes, not small synthetic patches. Many of our sampling strategies and design decisions require multiple large scenes to accurately validate. Unless someone chimes in with different opinions, it sounds like there is room for a research paper on this topic. Would love to include some GDAL developers on such a paper if anyone has interest. Will talk this over with my own research group. P.S. We've also been thinking about how to improve GPU support in GDAL. The lowest hanging fruit is anything that can be formulated as matrix multiplication, such as affine transformations in gdalwarp. Unfortunately, I don't know anything about CUDA/ROCm. If we were to do this in TorchGeo, we would just use PyTorch, which has a lot of overhead you won't need in GDAL. But let's discuss this in a different thread, don't want to derail this conversation. Dr. Adam J. Stewart Technical University of Munich School of Engineering and Design Data Science in Earth Observation On Feb 25, 2024, at 13:25, Even Rouault <even.roua...@spatialys.com> wrote: Adam, Automated performance regression testing is probably one of the aspect of testing that could be enhanced. While the GDAL autotest suite is quite comprehensive functionally wise, performance testing has traditionally been a bit lagging. That said, this is an aspect we have improved lately with the addition of a benchmark component to the autotest suite https://github.com/OSGeo/gdal/tree/master/autotest/benchmark . This is admitedly quite minimalistic for now, but testing some scenarios involving the GTiff driver and gdalwarp. To test non-regression for a pull request, we have a CI benchmark configuration (https://github.com/OSGeo/gdal/blob/master/.github/workflows/linux_build.yml#L111 + https://github.com/OSGeo/gdal/tree/master/.github/workflows/benchmarks) that runs the benchmarks first against master, and then with the pull request (during the same run of the same worker). But we need to allow a quite large tolerance threshold (up to +20%) to take into account that accurate timing measurements are extremely hard to get on CI infrastructure (even locally, on microbenchmarks this is very challenging). So this will mostly catch up big regressions, not subtle ones. One of the difficulty with benchmark testing is that we don't want the tests to run for hours, especially for pull requests, so they need to be written in a careful way to still trigger the relevant code paths & mechanisms of the code base that are exercised by real-world large datasets while running in a few seconds each at most. Typically those tests autogenerate their test data too, to avoid the test suite depending on too large datasets and keep the repository size as small as possible. As you mention GPUs, we have had private contacts from a couple GPU makers in recent years about potential GPU'ification of GDAL, but this has lead to nowhere for now. Some mentioned that moving data acquisition to the GPU could be interesting performance wise, but that seems to be a huge undertaking, basically moving the GTiff driver, libtiff and its codecs as GPU code. And even if done, how to manage the resulting code duplication... We aren't even able to properly keep up the OpenCL warper contributing many years ago in sync with the CPU warping code. We also lack GPU expertise in the current team to do that. Even Le 25/02/2024 à 12:58, Adam Stewart via gdal-dev a écrit : Hi, Background: I'm the developer of the TorchGeo<https://github.com/microsoft/torchgeo> software library. TorchGeo is a machine learning library that heavily relies on GDAL (via rasterio/fiona) for satellite imagery I/O. One of our primary concerns is ensuring that we can load data from disk fast enough to keep the GPU busy during model training. Of course, satellite imagery is often distributed in large files that make this challenging. We use various tricks to optimize performance (COGs, windowed reading, caching, compression, parallel workers, etc.). In our initial paper<https://arxiv.org/abs/2111.08872>, we chose to create our own arbitrary I/O benchmarking dataset composed of 100 Landsat scenes and 1 CDL map. See Figure 3 for the results, and Appendix A for the experiment details. Question: is there an official dataset that the GDAL developers use to benchmark GDAL itself? For example, if someone makes a change to how GDAL handles certain I/O operations, I assume the GDAL developers will benchmark it to see if I/O is now faster or slower. I'm envisioning experiments similar to https://kokoalberti.com/articles/geotiff-compression-optimization-guide/ for various file formats, compression levels, block sizes, etc. If such a dataset doesn't yet exist, I would be interested in creating one and publishing a paper on how this can be used to develop libraries like GDAL and TorchGeo. Dr. Adam J. Stewart Technical University of Munich School of Engineering and Design Data Science in Earth Observation _______________________________________________ gdal-dev mailing list gdal-dev@lists.osgeo.org<mailto:gdal-dev@lists.osgeo.org> https://lists.osgeo.org/mailman/listinfo/gdal-dev -- http://www.spatialys.com<http://www.spatialys.com/> My software is free, but my time generally not.
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