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@pentschev
Created August 9, 2019 20:27
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Blog Post - Parallelizing Custom CuPy Kernels with Dask - Output
res.compute()
array([[0.000000e+00, 2.000000e+00, 4.000000e+00, ..., 2.042000e+03,
2.044000e+03, 2.046000e+03],
[1.024000e+03, 1.026000e+03, 1.028000e+03, ..., 3.066000e+03,
3.068000e+03, 3.070000e+03],
[2.048000e+03, 2.050000e+03, 2.052000e+03, ..., 4.090000e+03,
4.092000e+03, 4.094000e+03],
...,
[4.191232e+06, 4.191234e+06, 4.191236e+06, ..., 4.193274e+06,
4.193276e+06, 4.193278e+06],
[4.192256e+06, 4.192258e+06, 4.192260e+06, ..., 4.194298e+06,
4.194300e+06, 4.194302e+06],
[4.193280e+06, 4.193282e+06, 4.193284e+06, ..., 4.195322e+06,
4.195324e+06, 4.195326e+06]], dtype=float32)
res_cupy
array([[0.000000e+00, 2.000000e+00, 4.000000e+00, ..., 2.042000e+03,
2.044000e+03, 2.046000e+03],
[1.024000e+03, 1.026000e+03, 1.028000e+03, ..., 3.066000e+03,
3.068000e+03, 3.070000e+03],
[2.048000e+03, 2.050000e+03, 2.052000e+03, ..., 4.090000e+03,
4.092000e+03, 4.094000e+03],
...,
[4.191232e+06, 4.191234e+06, 4.191236e+06, ..., 4.193274e+06,
4.193276e+06, 4.193278e+06],
[4.192256e+06, 4.192258e+06, 4.192260e+06, ..., 4.194298e+06,
4.194300e+06, 4.194302e+06],
[4.193280e+06, 4.193282e+06, 4.193284e+06, ..., 4.195322e+06,
4.195324e+06, 4.195326e+06]], dtype=float32)
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