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…party cuda arrays
Codecov Report
@@ Coverage Diff @@
## master #2860 +/- ##
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- Coverage 85.84% 85.79% -0.06%
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Files 326 327 +1
Lines 68311 68422 +111
Branches 7721 7729 +8
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+ Hits 58643 58703 +60
- Misses 8428 8481 +53
+ Partials 1240 1238 -2 |
Contributor
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I can confirm this works in combination with cupy/cupy#1144. import cupy
from numba import cuda
@cuda.jit
def add(x, y, out):
start = cuda.grid(1)
stride = cuda.gridsize(1)
for i in range(start, x.shape[0], stride):
out[i] = x[i] + y[i]
a = cupy.arange(10)
b = a * 2
out = cupy.empty_like(a)
print('out before:', out)
add[1, 32](a, b, out)
print('out after:', out)
print('array types:', type(a), type(b), type(out))Output: |
Contributor
|
It looks like support for |
Contributor
|
I think you forgot to check in |
Member
Author
|
Oops, added missing file now |
seibert
reviewed
May 17, 2018
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| In addition to the device arrays, numba can consume any object that implements | ||
| :ref:`cuda array interface <cuda-array-interface>`. These objects can be | ||
| converted into a device array by creating a view of the GPU buffer using the |
Contributor
There was a problem hiding this comment.
I think we should phrase this as "These objects also can be manually converted into a Numba device array by creating a view of the GPU buffer using the following APIs:"
seibert
reviewed
May 17, 2018
| .. autofunction:: numba.cuda.to_device | ||
| :noindex: | ||
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| In addition to the device arrays, numba can consume any object that implements |
Contributor
|
Two minor documentation nits, and then this looks good to merge. |
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for interop with 3rd party CUDA array.