def bind(self, array, update_device=True):
"""Create a new OffloadArray that is bound to an existing
numpy.ndarray. If update_device is False, then one needs to call
update_device() to issue a data transfer to the target device.
Otherwise, the OffloadArray will be in unspecified state on
the target device. Data transfers to the host overwrite the
data in the bound numpy.ndarray.
The operation is enqueued into the stream object and completes
asynchronously.
Parameters
----------
array : numpy.ndarray
Array to be bound to a new OffloadArray
update_device : bool, optional, default True
Control if the device buffer is updated with the array data
Returns
-------
out : OffloadArray
Instance of OffloadArray bound to the input array
See Also
--------
copy
Examples
--------
>>> a = numpy.zeros((2,2))
>>> oa = stream.bind(a)
>>> b = numpy.ones((1,1))
>>> ob = stream.bind(b, update_device=False)
>>> ob.update_device()
"""
if not isinstance(array, numpy.ndarray):
raise ValueError("only numpy.ndarray can be associated "
"with OffloadArray")
# detect the order of storage for 'array'
if array.flags.c_contiguous:
order = "C"
elif array.flags.f_contiguous:
order = "F"
else:
raise ValueError("could not detect storage order")
# construct and return a new OffloadArray
bound = pymic.OffloadArray(array.shape, array.dtype, order, False,
device=self._device, stream=self)
bound.array = array
# allocate the buffer on the device (and update data)
bound._device_ptr = self.allocate_device_memory(bound._nbytes)
if update_device:
bound.update_device()
return bound
def ones(self, shape, dtype=numpy.float, order='C', update_host=True):
"""Create a new OffloadArray that is bound to a numpy.ndarray
of the given shape and data type, and that is initialized with all
elements set to one of the corresponding data type. 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
----------
shape : int or tuple of int
Shape of the empty array
dtype : data-type, optional
Desired output data-type of the empty array
order : {'C', 'F'}, optional, default 'C'
Store multi-dimensional array data in row-major order
(C/C++, 'C') or column-major order (Fortran, 'F')
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_like, bcast, bcast_like
Examples
--------
>>> stream.ones((2,2))
array([[ 1., 1.],
[ 1., 1.]])
>>> o = stream.ones((2,2), update_host=False)
>>> o
array([[ 0.00000000e+000, -3.13334375e-294],
[ 2.77063169e-302, 1.16247550e-012]]) # random data
>>> o.update_host()
array([[ 1., 1.],
[ 1., 1.]])
"""
array = pymic.OffloadArray(shape,
dtype,
order,
device=self._device,
stream=self)
array.one()
if update_host:
array.update_host()
return array
def bcast(self, value, shape, dtype=numpy.float, order='C',
update_host=True):
"""Create a new OffloadArray that is bound to a numpy.ndarray
of the given shape and data type, and that is initialized with all
elements set to a given value of the corresponding data type. 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
shape : int or tuple of int
Shape of the empty array
dtype : data-type, optional
Desired output data-type of the empty array
order : {'C', 'F'}, optional, default 'C'
Store multi-dimensional array data in row-major order
(C/C++, 'C') or column-major order (Fortran, 'F')
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 set to a
given value
See Also
--------
empty, empty_like, zeros, zeros_like, ones, ones_like, bcast_like
Examples
--------
>>> stream.bcast(42.0, (2,2))
array([[ 42., 42.],
[ 42., 42.]])
>>> o = stream.bcast(42.0, (2,2), update_host=False)
>>> o
array([[ 0.00000000e+000, 3.24370112e+178],
[ 2.53946330e-292, 6.52391278e+091]]) # random data
>>> o.update_host()
array([[ 42., 42.],
[ 42., 42.]])
"""
array = pymic.OffloadArray(shape, dtype, order, device=self._device,
stream=self)
array.fill(value) # TODO: here the stream might become an issue
if update_host:
array.update_host()
return array
def empty(self, shape, dtype=numpy.float, order='C', update_host=True):
"""Create a new OffloadArray that is bound to an empty numpy.ndarray
of the given shape and data type. 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
----------
shape : int or tuple of int
Shape of the empty array
dtype : data-type, optional
Desired output data-type of the empty array
order : {'C', 'F'}, optional, default 'C'
Store multi-dimensional array data in row-major order
(C/C++, 'C') or column-major order (Fortran, 'F')
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 bound to the input array
See Also
--------
empty_like, zeros, zeros_like, ones, ones_like, bcast, bcast_like
Examples
--------
>>> stream.empty((2,2))
array([[ 4.41694687e-321, 6.92646058e-310],
[ 4.71338626e-321, 6.92646058e-310]]) # random data
>>> o = stream.empty((2,2), update_host=False)
>>> o
array([[ 0.00000000e+000, -3.13334375e-294],
[ 2.77063169e-302, 1.76125728e-312]]) # random data
>>> o.update_host()
array([[ 4.26872718e-321, 6.92050581e-310],
[ 4.34283703e-321, 6.92050581e-310]]) # random data
"""
array = pymic.OffloadArray(shape,
dtype,
order,
device=self._device,
stream=self)
if update_host:
array.update_host()
return array
