def minimum_cl(a, b=None):
""" Minimum values of two GPUArrays.
Parameters
----------
a : gpuarray
First GPUArray.
b : gpuarray
Second GPUArray.
Returns
-------
gpuarray
Minimum values from both GPArrays, or single value if one GPUarray.
Examples
--------
>>> a = minimum_cl(give_cl(queue, [1, 2, 3]), give_cl(queue, [3, 2, 1]))
[1, 2, 1]
>>> type(a)
<class 'pyopencl.array.Array'>
"""
if b is not None:
return cl_array.minimum(a, b)
return cl_array.min(a)
def minimum(t: Tensor, uts: Union[Tensor, Scalar]) -> Tensor:
"""Returns the minimum of a tensor."""
if t.gpu:
if not isinstance(uts, Tensor):
ot: cl.array.Array = clarray.empty(QUEUE,
t.shape,
dtype=np.float32).fill(uts)
return Tensor(clarray.minimum(t._data, ot), gpu=True)
return Tensor(clarray.minimum(t._data, uts._data), gpu=True)
if not isinstance(uts, Tensor):
return Tensor(np.minimum(t._data, uts))
return Tensor(np.minimum(t._data, uts._data))
def test_if_positive(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
l = 20000
a_gpu = clrand(context, queue, (l,), numpy.float32)
b_gpu = clrand(context, queue, (l,), numpy.float32)
a = a_gpu.get()
b = b_gpu.get()
max_a_b_gpu = cl_array.maximum(a_gpu, b_gpu)
min_a_b_gpu = cl_array.minimum(a_gpu, b_gpu)
print(max_a_b_gpu)
print(numpy.maximum(a, b))
assert la.norm(max_a_b_gpu.get()- numpy.maximum(a, b)) == 0
assert la.norm(min_a_b_gpu.get()- numpy.minimum(a, b)) == 0
def test_if_positive(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
ary_len = 20000
a_gpu = clrand(queue, (ary_len,), np.float32)
b_gpu = clrand(queue, (ary_len,), np.float32)
a = a_gpu.get()
b = b_gpu.get()
max_a_b_gpu = cl_array.maximum(a_gpu, b_gpu)
min_a_b_gpu = cl_array.minimum(a_gpu, b_gpu)
print(max_a_b_gpu)
print(np.maximum(a, b))
assert la.norm(max_a_b_gpu.get() - np.maximum(a, b)) == 0
assert la.norm(min_a_b_gpu.get() - np.minimum(a, b)) == 0
def test_if_positive(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
l = 20000
a_gpu = clrand(context, queue, (l, ), numpy.float32)
b_gpu = clrand(context, queue, (l, ), numpy.float32)
a = a_gpu.get()
b = b_gpu.get()
max_a_b_gpu = cl_array.maximum(a_gpu, b_gpu)
min_a_b_gpu = cl_array.minimum(a_gpu, b_gpu)
print max_a_b_gpu
print numpy.maximum(a, b)
assert la.norm(max_a_b_gpu.get() - numpy.maximum(a, b)) == 0
assert la.norm(min_a_b_gpu.get() - numpy.minimum(a, b)) == 0
def test_if_positive(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
ary_len = 20000
a_gpu = clrand(queue, (ary_len,), np.float32)
b_gpu = clrand(queue, (ary_len,), np.float32)
a = a_gpu.get()
b = b_gpu.get()
max_a_b_gpu = cl_array.maximum(a_gpu, b_gpu)
min_a_b_gpu = cl_array.minimum(a_gpu, b_gpu)
print(max_a_b_gpu)
print(np.maximum(a, b))
assert la.norm(max_a_b_gpu.get() - np.maximum(a, b)) == 0
assert la.norm(min_a_b_gpu.get() - np.minimum(a, b)) == 0
def minimum(self, x, y):
import pyopencl.array as cl_array
return cl_array.minimum(x, y)
