def bcast_like(self, value, other, update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
that matches shape and type of an existing OffloadArray or
numpy.ndarray. If update_host is True (the default), then the empty
array is automatically transferred to the host numpy.ndarray. If
set to False, the data transfer is avoided and the data in the host
array is in undefined state.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
value : scalar type
Value to broadcast to all elements of the created array
other : numpy.ndarray
Template array used to determine shape, data type, and storage
order
update_host : bool, optional, default True
Control if the host array is updated with the array data
during construction of the OffloadArray
Returns
-------
out : OffloadArray
Instance of OffloadArray with all elements being one
See Also
--------
empty, empty_like, zeros, zeros_like, ones, bcast, bcast_like
Examples
--------
>>> a = numpy.zeros((2,2))
>>> stream.bcast_like(42.0, a)
array([[ 1., 1.],
[ 1., 1.]])
>>> o = stream.bcast_like(42.0, a, update_host=False)
>>> o
array([[ 0.00000000e+000, 1.32102240e-312],
[ 1.43679049e+161, 1.16250014e-012]]) # random data
>>> o.update_host()
array([[ 42., 42.],
[ 42., 42.]])
"""
if (not isinstance(other, numpy.ndarray)
and not isinstance(other, pymic.OffloadArray)):
raise ValueError("only numpy.ndarray can be used "
"with this function")
return self.bcast(value,
other.shape,
other.dtype,
get_order(other),
update_host=update_host)
def bcast_like(self, value, other, update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
that matches shape and type of an existing OffloadArray or
numpy.ndarray. If update_host is True (the default), then the empty
array is automatically transferred to the host numpy.ndarray. If
set to False, the data transfer is avoided and the data in the host
array is in undefined state.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
value : scalar type
Value to broadcast to all elements of the created array
other : numpy.ndarray
Template array used to determine shape, data type, and storage
order
update_host : bool, optional, default True
Control if the host array is updated with the array data
during construction of the OffloadArray
Returns
-------
out : OffloadArray
Instance of OffloadArray with all elements being one
See Also
--------
empty, empty_like, zeros, zeros_like, ones, bcast, bcast_like
Examples
--------
>>> a = numpy.zeros((2,2))
>>> stream.bcast_like(42.0, a)
array([[ 1., 1.],
[ 1., 1.]])
>>> o = stream.bcast_like(42.0, a, update_host=False)
>>> o
array([[ 0.00000000e+000, 1.32102240e-312],
[ 1.43679049e+161, 1.16250014e-012]]) # random data
>>> o.update_host()
array([[ 42., 42.],
[ 42., 42.]])
"""
if (not isinstance(other, numpy.ndarray) and
not isinstance(other, pymic.OffloadArray)):
raise ValueError("only numpy.ndarray can be used "
"with this function")
return self.bcast(value, other.shape, other.dtype,
get_order(other), update_host=update_host)
def zeros_like(self, other, dtype=None, update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
that matches shape and type of an existing OffloadArray or
numpy.ndarray. If update_host is True (the default), then the empty
array is automatically transferred to the host numpy.ndarray. If
set to False, the data transfer is avoided and the data in the host
array is in undefined state.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
other : numpy.ndarray
Template array used to determine shape, data type, and storage
order
update_host : bool, optional, default True
Control if the host array is updated with the array data
during construction of the OffloadArray
Returns
-------
out : OffloadArray
Instance of OffloadArray with all elements being zero
See Also
--------
empty, empty_like, zeros, ones, ones_like, bcast, bcast_like
Examples
--------
>>> a = numpy.ones((2,2))
>>> stream.zeros_like(a)
array([[ 0., 0.],
[ 0., 0.]])
>>> o = stream.zeros_like(a, update_host=False)
>>> o
array([[ 1.12110451e-316, 1.32102240e-312],
[ 6.94088686e-310, 6.94088688e-310]]) # random data
>>> o.update_host()
array([[ 0., 0.],
[ 0., 0.]])
"""
if (not isinstance(other, numpy.ndarray)
and not isinstance(other, pymic.OffloadArray)):
raise ValueError("only numpy.ndarray can be used "
"with this function")
_dtype = other.dtype if dtype is None else dtype
return self.zeros(other.shape, _dtype, get_order(other),
update_host=update_host)
def empty_like(self, other, update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
that matches shape and type of an existing OffloadArray or
numpy.ndarray. If update_host is True (the default), then the empty
array is automatically transferred to the host numpy.ndarray. If
set to False, the data transfer is avoided and the data in the host
array is in undefined state.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
other : numpy.ndarray
Template array used to determine shape, data type, and storage
order
update_host :
Control if the host array is updated with the array data
during construction of the OffloadArray
See Also
--------
empty, zeros, zeros_like, ones, ones_like, bcast, bcast_like
Examples
--------
>>> a = numpy.ones((2,2))
>>> stream.empty_like(a)
array([[ 4.26872718e-321, 6.91990916e-310],
[ 4.34283703e-321, 6.91990916e-310]]) # random data
>>> o = stream.empty_like(a, update_host=False)
>>> o
array([[ 1.12110451e-316, 1.32102240e-312],
[ 6.94088686e-310, 6.94088688e-310]]) # random data
>>> o.update_host()
array([[ 4.26872718e-321, 6.95150287e-310],
[ 4.34283703e-321, 6.95150287e-310]]) # random data
"""
if (not isinstance(other, numpy.ndarray)
and not isinstance(other, pymic.OffloadArray)):
raise ValueError("only numpy.ndarray can be used "
"with this function")
return self.empty(other.shape,
other.dtype,
get_order(other),
update_host=update_host)
def empty_like(self, other, update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
that matches shape and type of an existing OffloadArray or
numpy.ndarray. If update_host is True (the default), then the empty
array is automatically transferred to the host numpy.ndarray. If
set to False, the data transfer is avoided and the data in the host
array is in undefined state.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
other : numpy.ndarray
Template array used to determine shape, data type, and storage
order
update_host :
Control if the host array is updated with the array data
during construction of the OffloadArray
See Also
--------
empty, zeros, zeros_like, ones, ones_like, bcast, bcast_like
Examples
--------
>>> a = numpy.ones((2,2))
>>> stream.empty_like(a)
array([[ 4.26872718e-321, 6.91990916e-310],
[ 4.34283703e-321, 6.91990916e-310]]) # random data
>>> o = stream.empty_like(a, update_host=False)
>>> o
array([[ 1.12110451e-316, 1.32102240e-312],
[ 6.94088686e-310, 6.94088688e-310]]) # random data
>>> o.update_host()
array([[ 4.26872718e-321, 6.95150287e-310],
[ 4.34283703e-321, 6.95150287e-310]]) # random data
"""
if (not isinstance(other, numpy.ndarray) and
not isinstance(other, pymic.OffloadArray)):
raise ValueError("only numpy.ndarray can be used "
"with this function")
return self.empty(other.shape, other.dtype, get_order(other),
update_host=update_host)