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CUDA: Skip multi-GPU copy test with peer access disabled #7266

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Merged
sklam merged 1 commit into from
Aug 11, 2021
Merged

CUDA: Skip multi-GPU copy test with peer access disabled #7266

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29 changes: 26 additions & 3 deletions numba/cuda/tests/cudapy/test_multigpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,10 +107,33 @@ def vector_add_scalar(arr, val):
with cuda.gpus[1]:
np.testing.assert_equal(arr2.copy_to_host(), (hostarr + 2))

@unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
def test_with_context_peer_copy(self):
# Peer access is not always possible - for example, with one GPU in TCC
# mode and one in WDDM - if that is the case, this test would fail so
# we need to skip it.
with cuda.gpus[0]:
ctx = cuda.current_context()
if not ctx.can_access_peer(1):
self.skipTest('Peer access between GPUs disabled')

# 1. Create a range in an array
hostarr = np.arange(10, dtype=np.float32)

# 2. Copy range array from host -> GPU 0
with cuda.gpus[0]:
# Transfer from GPU1 to GPU0
arr1.copy_to_device(arr2)
np.testing.assert_equal(arr1.copy_to_host(), (hostarr + 2))
arr1 = cuda.to_device(hostarr)

# 3. Initialize a zero-filled array on GPU 1
with cuda.gpus[1]:
arr2 = cuda.to_device(np.zeros_like(hostarr))

with cuda.gpus[0]:
# 4. Copy range from GPU 0 -> GPU 1
arr2.copy_to_device(arr1)

# 5. Copy range from GPU 1 -> host and check contents
np.testing.assert_equal(arr2.copy_to_host(), hostarr)


if __name__ == '__main__':
Expand Down