def _allocate(self,
where,
obj=None,
dtype=None,
shape=None,
strides=None,
copy=True):
if dtype:
dtype = np.dtype(dtype)
if where == 'host':
if obj is not None:
self._host = np.array(obj, dtype, copy=copy)
else:
self._host = np.empty(shape, dtype,
_s2o(dtype, shape, strides))
else:
# Don't import this at module-scope as it may not be available
# in all environments (e.g., CUDASIM)
from numba.cuda.cudadrv import devicearray as da
if obj is not None:
# If 'obj' is an array-like object but not an ndarray,
# construct an ndarray first to extract all the parameters we need.
if not isinstance(obj, np.ndarray):
obj = np.array(obj, copy=False)
self._gpu = da.from_array_like(obj)
else:
if strides is None:
strides = _o2s(dtype, shape, 'C')
self._gpu = da.DeviceNDArray(shape, strides, dtype)
def _allocate(self, where, obj=None, dtype=None, shape=None, strides=None,
copy=True):
if dtype:
dtype = np.dtype(dtype)
if where == 'host':
if obj is not None:
self._host = np.array(obj, dtype, copy=copy)
else:
self._host = np.empty(shape, dtype, _s2o(dtype, shape, strides))
else:
# Don't import this at module-scope as it may not be available
# in all environments (e.g., CUDASIM)
from numba.cuda.cudadrv import devicearray as da
if obj is not None:
# If 'obj' is an array-like object but not an ndarray,
# construct an ndarray first to extract all the parameters we need.
if not isinstance(obj, np.ndarray):
obj = np.array(obj, copy=False)
self._gpu = da.from_array_like(obj)
else:
if strides is None:
strides = _o2s(dtype, shape, 'C')
self._gpu = da.DeviceNDArray(shape, strides, dtype)