def test_gpu_array_interleaved(self):
@cuda.jit('void(double[:], double[:])')
def copykernel(x, y):
i = cuda.grid(1)
if i < x.shape[0]:
x[i] = i
y[i] = i
x = np.arange(10, dtype=np.double)
y = x[:-1:2]
# z = x[1::2]
# n = y.size
try:
cuda.devicearray.auto_device(y)
except ValueError:
pass
else:
raise AssertionError("Should raise exception complaining the "
"contiguous-ness of the array.")
# Should we handle this use case?
# assert z.size == y.size
# copykernel[1, n](y, x)
# print(y, z)
# assert np.all(y == z)
# assert np.all(y == list(range(n)))
if __name__ == '__main__':
unittest.main()
return_value=None) as mock_sync:
f[1, 1](f_arr1, f_arr2)
# Ensure that synchronize was called twice
mock_sync.assert_has_calls([call(), call()])
def test_launch_sync_disabled(self):
# Create two foreign arrays with streams
s1 = cuda.stream()
s2 = cuda.stream()
f_arr1 = ForeignArray(cuda.device_array(10, stream=s1))
f_arr2 = ForeignArray(cuda.device_array(10, stream=s2))
with override_config('CUDA_ARRAY_INTERFACE_SYNC', False):
@cuda.jit
def f(x, y):
pass
with patch.object(cuda.cudadrv.driver.Stream,
'synchronize',
return_value=None) as mock_sync:
f[1, 1](f_arr1, f_arr2)
# Ensure that synchronize was not called
mock_sync.assert_not_called()
if __name__ == "__main__":
unittest.main()
