def test_mdot_matrix_complex128(self):
a = np.asarray(np.random.rand(4, 2), np.complex128)
b = np.asarray(np.random.rand(2, 2), np.complex128)
c = np.asarray(np.random.rand(2, 2), np.complex128)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
d_gpu = linalg.mdot(a_gpu, b_gpu, c_gpu)
assert np.allclose(np.dot(a, np.dot(b, c)), d_gpu.get())
def test_mdot_matrix_complex128(self):
a = np.asarray(np.random.rand(4, 2), np.complex128)
b = np.asarray(np.random.rand(2, 2), np.complex128)
c = np.asarray(np.random.rand(2, 2), np.complex128)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
d_gpu = linalg.mdot(a_gpu, b_gpu, c_gpu)
assert np.allclose(np.dot(a, np.dot(b, c)), d_gpu.get())
import pycuda.autoinit
import numpy as np
import scikits.cuda.linalg as linalg
import scikits.cuda.misc as cumisc
linalg.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 multiple matrix multiplication for type ' + str(np.dtype(t))
if np.iscomplexobj(t()):
a = np.asarray(np.random.rand(8, 4) + 1j * np.random.rand(8, 4), t)
b = np.asarray(np.random.rand(4, 4) + 1j * np.random.rand(4, 4), t)
c = np.asarray(np.random.rand(4, 4) + 1j * np.random.rand(4, 4), t)
else:
a = np.asarray(np.random.rand(8, 4), t)
b = np.asarray(np.random.rand(4, 4), t)
c = np.asarray(np.random.rand(4, 4), t)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
d_gpu = linalg.mdot(a_gpu, b_gpu, c_gpu)
print 'Success status: ', np.allclose(np.dot(a, np.dot(b, c)), d_gpu.get())
import pycuda.gpuarray as gpuarray import pycuda.autoinit import numpy as np import scikits.cuda.linalg as linalg linalg.init() a = np.asarray(np.random.rand(4, 2), np.float32) b = np.asarray(np.random.rand(2, 2), np.float32) c = np.asarray(np.random.rand(2, 2), np.float32) a_gpu = gpuarray.to_gpu(a) b_gpu = gpuarray.to_gpu(b) c_gpu = gpuarray.to_gpu(c) d_gpu = linalg.mdot(a_gpu, b_gpu, c_gpu) print(d_gpu) np.allclose(np.dot(a, np.dot(b, c)), d_gpu.get())
