def test_transpose_float64(self):
# M < N
a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
# M > N
b = a.T.copy()
b_gpu = gpuarray.to_gpu(b)
bt_gpu = linalg.transpose(b_gpu)
assert np.all(b.T == bt_gpu.get())
def test_transpose_complex128(self):
# M < N
a = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]], np.complex128)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
# M > N
b = a.T.copy()
b_gpu = gpuarray.to_gpu(b)
bt_gpu = linalg.transpose(b_gpu)
assert np.all(b.T == bt_gpu.get())
def test_transpose_float64(self):
# M < N
a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
# M > N
b = a.T.copy()
b_gpu = gpuarray.to_gpu(b)
bt_gpu = linalg.transpose(b_gpu)
assert np.all(b.T == bt_gpu.get())
def test_transpose_complex128(self):
# M < N
a = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]],
np.complex128)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
# M > N
b = a.T.copy()
b_gpu = gpuarray.to_gpu(b)
bt_gpu = linalg.transpose(b_gpu)
assert np.all(b.T == bt_gpu.get())
def test_transpose_complex64(self):
a = np.array([[1j, 2j, 3j, 4j, 5j, 6j],
[7j, 8j, 9j, 10j, 11j, 12j]],
np.complex64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
def test_transpose_float64(self):
a = np.array([[1, 2, 3, 4, 5, 6],
[7, 8, 9, 10, 11, 12]],
np.float64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
import numpy as np
import scikits.cuda.linalg as culinalg
import scikits.cuda.misc as cumisc
culinalg.init()
# Double precision is only supported by devices with compute
# capability >= 1.3:
import string
demo_types = [np.float32, np.complex64]
if cumisc.get_compute_capability(pycuda.autoinit.device) >= 1.3:
demo_types.extend([np.float64, np.complex128])
for t in demo_types:
print 'Testing transpose for type ' + str(np.dtype(t))
if np.iscomplexobj(t()):
b = np.array([[1j, 2j, 3j, 4j, 5j, 6j],
[7j, 8j, 9j, 10j, 11j, 12j]], t)
else:
a = np.array([[1, 2, 3, 4, 5, 6],
[7, 8, 9, 10, 11, 12]], t)
a_gpu = gpuarray.to_gpu(a)
at_gpu = culinalg.transpose(a_gpu)
if np.iscomplexobj(t()):
print 'Success status: ', np.all(np.conj(a.T) == at_gpu.get())
else:
print 'Success status: ', np.all(a.T == at_gpu.get())
import pycuda.autoinit
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import numpy as np
import scikits.cuda.linalg as culinalg
import scikits.cuda.misc as cumisc
culinalg.init()
# Double precision is only supported by devices with compute
# capability >= 1.3:
import string
demo_types = [np.float32, np.complex64]
if cumisc.get_compute_capability(pycuda.autoinit.device) >= 1.3:
demo_types.extend([np.float64, np.complex128])
for t in demo_types:
print 'Testing transpose for type ' + str(np.dtype(t))
if np.iscomplexobj(t()):
b = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]],
t)
else:
a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], t)
a_gpu = gpuarray.to_gpu(a)
at_gpu = culinalg.transpose(a_gpu)
if np.iscomplexobj(t()):
print 'Success status: ', np.all(np.conj(a.T) == at_gpu.get())
else:
print 'Success status: ', np.all(a.T == at_gpu.get())
def test_transpose_complex64(self):
a = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]],
np.complex64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
def test_transpose_float64(self):
a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float64)
a_gpu = gpuarray.to_gpu(a)
at_gpu = linalg.transpose(a_gpu)
assert np.all(a.T == at_gpu.get())
#!/usr/bin/env python """ Demonstrates how to transpose matrices on the GPU. """ import pycuda.autoinit import pycuda.driver as drv import pycuda.gpuarray as gpuarray import numpy as np import scikits.cuda.linalg as linalg linalg.init() a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float32) a_gpu = gpuarray.to_gpu(a) at_gpu = linalg.transpose(a_gpu, pycuda.autoinit.device) print 'Success status: ', np.all(a.T == at_gpu.get()) b = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]], np.complex64) b_gpu = gpuarray.to_gpu(b) bt_gpu = linalg.transpose(b_gpu, pycuda.autoinit.device) print 'Success status: ', np.all(np.conj(b.T) == bt_gpu.get())
