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Great feature of this method is you can use CUDA-C/C++ inline in python code and many code may be ported just replacing
import numpy as np
to
import cupy as np
but may need a tune. For example: I stuck recursive using np.arange() in vectorize and found than one can use CUDA-C/C++ with for/next and other great feature of C/C++.
Sverchok 1.3.0-alpha, Blender 3.6.3, Windows 11.
Performance test numpy/CUDA with node "Generators->Generators Extended->Spiral", mode "Spiral Cornu".
sverchok/nodes/generators_extended/spiral_mk2.py
Lines 301 to 305 in fad7b2c
Great feature of this method is you can use CUDA-C/C++ inline in python code and many code may be ported just replacing
import numpy as np
to
import cupy as np
but may need a tune. For example: I stuck recursive using np.arange() in vectorize and found than one can use CUDA-C/C++ with for/next and other great feature of C/C++.
See:
https://cupy.dev/
An Easy Introduction to CUDA C and C++: https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/
CUDA C++ Programming Guide: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
cupy.ElementwiseKernel: https://docs.cupy.dev/en/stable/reference/generated/cupy.ElementwiseKernel.html
P.S.
May be this technology will incredibly level up of performance of Sverchok?
P.P.S.
I try to use 12.x library cuda-cupy12x:
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