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BLI: refactor IndexMask for better performance and memory usage
Goals of this refactor: * Reduce memory consumption of `IndexMask`. The old `IndexMask` uses an `int64_t` for each index which is more than necessary in pretty much all practical cases currently. Using `int32_t` might still become limiting in the future in case we use this to index e.g. byte buffers larger than a few gigabytes. We also don't want to template `IndexMask`, because that would cause a split in the "ecosystem", or everything would have to be implemented twice or templated. * Allow for more multi-threading. The old `IndexMask` contains a single array. This is generally good but has the problem that it is hard to fill from multiple-threads when the final size is not known from the beginning. This is commonly the case when e.g. converting an array of bool to an index mask. Currently, this kind of code only runs on a single thread. * Allow for efficient set operations like join, intersect and difference. It should be possible to multi-thread those operations. * It should be possible to iterate over an `IndexMask` very efficiently. The most important part of that is to avoid all memory access when iterating over continuous ranges. For some core nodes (e.g. math nodes), we generate optimized code for the cases of irregular index masks and simple index ranges. To achieve these goals, a few compromises had to made: * Slicing of the mask (at specific indices) and random element access is `O(log #indices)` now, but with a low constant factor. It should be possible to split a mask into n approximately equally sized parts in `O(n)` though, making the time per split `O(1)`. * Using range-based for loops does not work well when iterating over a nested data structure like the new `IndexMask`. Therefor, `foreach_*` functions with callbacks have to be used. To avoid extra code complexity at the call site, the `foreach_*` methods support multi-threading out of the box. The new data structure splits an `IndexMask` into an arbitrary number of ordered `IndexMaskSegment`. Each segment can contain at most `2^14 = 16384` indices. The indices within a segment are stored as `int16_t`. Each segment has an additional `int64_t` offset which allows storing arbitrary `int64_t` indices. This approach has the main benefits that segments can be processed/constructed individually on multiple threads without a serial bottleneck. Also it reduces the memory requirements significantly. For more details see comments in `BLI_index_mask.hh`. I did a few tests to verify that the data structure generally improves performance and does not cause regressions: * Our field evaluation benchmarks take about as much as before. This is to be expected because we already made sure that e.g. add node evaluation is vectorized. The important thing here is to check that changes to the way we iterate over the indices still allows for auto-vectorization. * Memory usage by a mask is about 1/4 of what it was before in the average case. That's mainly caused by the switch from `int64_t` to `int16_t` for indices. In the worst case, the memory requirements can be larger when there are many indices that are very far away. However, when they are far away from each other, that indicates that there aren't many indices in total. In common cases, memory usage can be way lower than 1/4 of before, because sub-ranges use static memory. * For some more specific numbers I benchmarked `IndexMask::from_bools` in `index_mask_from_selection` on 10.000.000 elements at various probabilities for `true` at every index: ``` Probability Old New 0 4.6 ms 0.8 ms 0.001 5.1 ms 1.3 ms 0.2 8.4 ms 1.8 ms 0.5 15.3 ms 3.0 ms 0.8 20.1 ms 3.0 ms 0.999 25.1 ms 1.7 ms 1 13.5 ms 1.1 ms ``` Pull Request: https://projects.blender.org/blender/blender/pulls/104629
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