Forward-merge branch-24.12 into branch-25.02 #1488
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Improves on a performance regression identified by @trxcllnt when using `quadtree_point_in_polygon` Seems like the core slowdown is that the passed `points` is a `GeoSeries`, and the x/y points are generated by interleaving them in `GeoColumnAccessor.xy` and then slicing x/y back out. Since these points appear to be held in a `cudf.ListDtype` (i.e. `[x, y]`), I tried avoiding interleaving and slicing the the x and y points individually in the lists using the `cudf.Series.list.get` API but this was failing for x/y points for `.polygons.x`/`.polygons.y` since some cuspatial APIs expect these points to be `float` (not sure if it's a cudf bug in `list.get` or how points as structured for polygons) Instead, I was able to get a 5x speedup by * Caching the interleaving step to avoid doing this multiple times * Slicing a cupy array of points instead of a `cudf.Series` (which uselessly slices an `.index` which is not used) <details> <summary>Benchmarking code</summary> ```python # 1.3G points.arrow # 722K polys.arrow import cudf import cupy import cuspatial import pyarrow as pa from time import perf_counter_ns polys = pa.ipc.open_stream("polys.arrow").read_all() polys = cudf.DataFrame.from_arrow(polys).rename(columns={"tract": "poly"}) point = polys["poly"].list.leaves.struct.explode() polys = cuspatial.GeoSeries.from_polygons_xy( point.interleave_columns(), polys["poly"]._column.elements.offsets, polys["poly"]._column.offsets, cupy.arange(0, len(polys) + 1) ) point = pa.ipc.open_stream("points.arrow").read_all() point = cudf.DataFrame.from_arrow(point) min_x = point["x"].min() max_x = point["x"].max() min_y = point["y"].min() max_y = point["y"].max() max_size = 0 max_depth = 16 threshold = 10 while max_size <= threshold: max_depth -= 1 max_size = len(point) / pow(4, max_depth) / 4 scale = max(max_x - min_x, max_y - min_y) / (1 << max_depth) point_xy = cuspatial.GeoSeries.from_points_xy(point.interleave_columns()) point_map, quadtree = ( cuspatial.quadtree_on_points(point_xy, min_x, max_x, min_y, max_y, scale, max_depth, max_size)) t0 = perf_counter_ns() cuspatial.quadtree_point_in_polygon( cuspatial.join_quadtree_and_bounding_boxes( quadtree, cuspatial.polygon_bounding_boxes(polys[0:1]), min_x, max_x, min_y, max_y, scale, max_depth ), quadtree, point_map, point_xy, polys[0:1] ) t1 = perf_counter_ns() print(f"{(t1 - t0) / (10 ** 6)}ms") t0 = perf_counter_ns() poly_offsets = cudf.Series(polys[0:1].polygons.part_offset)._column ring_offsets = cudf.Series(polys[0:1].polygons.ring_offset)._column poly_points_x = cudf.Series(polys[0:1].polygons.x)._column poly_points_y = cudf.Series(polys[0:1].polygons.y)._column from cuspatial._lib import spatial_join cudf.DataFrame._from_data( *spatial_join.quadtree_point_in_polygon( cuspatial.join_quadtree_and_bounding_boxes( quadtree, cuspatial.polygon_bounding_boxes(polys[0:1]), min_x, max_x, min_y, max_y, scale, max_depth ), quadtree, point_map._column, point["x"]._column, point["y"]._column, poly_offsets, ring_offsets, poly_points_x, poly_points_y, ) ) t1 = perf_counter_ns() print(f"{(t1 - t0) / (10 ** 6)}ms") # 127.406344ms # this PR # 644.963021ms # branch 24.10 ``` </details> Authors: - Matthew Roeschke (https://github.com/mroeschke) Approvers: - Paul Taylor (https://github.com/trxcllnt) - Mark Harris (https://github.com/harrism) URL: #1446
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SUCCESS - forward-merge complete. |
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Forward-merge triggered by push to branch-24.12 that creates a PR to keep branch-25.02 up-to-date. If this PR is unable to be immediately merged due to conflicts, it will remain open for the team to manually merge. See forward-merger docs for more info.