Speed up cost_distance iterative tile Dijkstra 2-4x#1023
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Batch-compute all dask tiles in a single scheduler pass and cache them for reuse across iterations, replacing per-tile .compute() calls that re-executed the dask graph each time. Store friction boundaries as float64 to skip repeated dtype conversion. Assemble the final result eagerly from cached tiles instead of through da.map_blocks. Pass precomputed f_min from dask+cupy fallback to avoid a redundant da.nanmin().compute(). Benchmarked improvement on the iterative (unbounded max_cost) path: 200x100: 0.206s -> 0.050s (4.1x), 1.90MB -> 0.80MB (-58%) 300x150: 0.229s -> 0.075s (3.0x), 2.91MB -> 1.38MB (-53%) 400x200: 0.263s -> 0.114s (2.3x), 4.02MB -> 2.19MB (-46%) numpy and dask-bounded (map_overlap) paths are unchanged.
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Summary
dask.compute()call and cache them, replacing per-tile.compute()calls that re-executed the graph on every iterationda.map_blocks_compute_seedsf_minfrom the dask+cupy fallback path to_cost_distance_dask, avoiding a redundantda.nanmin().compute()Benchmarked improvement on the iterative (unbounded
max_cost) dask path:numpy and dask-bounded (
map_overlap) paths are unchanged.Test plan
test_cost_distance.pytests pass_f_mincorrectly (requires GPU)