This issue discusses performance comparison for multidimensional grid interpolation. GridInterpolations.jl is compared to interpolation done using scipy in Python.
2D interpolation of a 3x3 grid for 10,000 points:
GridInterpolations.jl with Simplex: ~0.065s
Scipy Interpolation: ~0.5s
3D interpolation of a 3x3x3 grid for 1,000,000 points:
GridInterpolations.jl with Simplex: ~3.8s
Scipy Interpolation: ~54s
GridInterpolations.jl is ~7.5X faster for 2D interpolation.
GridInterpolations.jl is ~14X faster for 3D interpolation.
Some details about the scipy implementation:
Using scipy.spatial.Delaunay for triangulation (not timing this part)
Using scipy.interpolate.LinearNDInterpolator for the interpolation (timing only this part)
This issue discusses performance comparison for multidimensional grid interpolation. GridInterpolations.jl is compared to interpolation done using scipy in Python.
2D interpolation of a 3x3 grid for 10,000 points: GridInterpolations.jl with Simplex: ~0.065s Scipy Interpolation: ~0.5s
3D interpolation of a 3x3x3 grid for 1,000,000 points: GridInterpolations.jl with Simplex: ~3.8s Scipy Interpolation: ~54s
GridInterpolations.jl is ~7.5X faster for 2D interpolation. GridInterpolations.jl is ~14X faster for 3D interpolation.
Some details about the scipy implementation: