1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
|
#!/usr/bin/env python
"""
Demo of how to call low-level CUSOLVER wrappers to perform SVD decomposition.
"""
import numpy as np
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import skcuda.cusolver as solver
h = solver.cusolverDnCreate()
x = np.asarray([[1.80, 2.88, 2.05, -0.89],
[5.25, -2.95, -0.95, -3.80],
[1.58, -2.69, -2.90, -1.04],
[-1.11, -0.66, -0.59, 0.80]]).astype(np.float32)
# Need to reverse dimensions because CUSOLVER expects column-major matrices:
n, m = x.shape
x_gpu = gpuarray.to_gpu(x)
# Set up work buffers:
Lwork = solver.cusolverDnSgesvd_bufferSize(h, m, n)
workspace_gpu = gpuarray.zeros(Lwork, np.float32)
devInfo_gpu = gpuarray.zeros(1, np.int32)
# Set up output buffers:
s_gpu = gpuarray.zeros(min(m, n), np.float32)
u_gpu = gpuarray.zeros((m, m), np.float32)
vh_gpu = gpuarray.zeros((n, n), np.float32)
# Compute:
status = solver.cusolverDnSgesvd(h, 'A', 'A', m, n, x_gpu.gpudata, m, s_gpu.gpudata,
u_gpu.gpudata, m, vh_gpu.gpudata, n,
workspace_gpu.gpudata, Lwork, 0, devInfo_gpu.gpudata)
# Confirm that solution is correct by ensuring that the original matrix can be
# obtained from the decomposition:
print('correct solution: %r' %
np.allclose(x, np.dot(vh_gpu.get(), np.dot(np.diag(s_gpu.get()),
u_gpu.get())), 1e-4))
solver.cusolverDnDestroy(h)
|
