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#!/usr/bin/env python
"""
Demonstrates how to transpose matrices on the GPU.
"""
from __future__ import print_function
import pycuda.autoinit
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import numpy as np
import skcuda.linalg as culinalg
import skcuda.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()))
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