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#!/usr/bin/env python
"""
Demonstrates multiplication of two matrices on the GPU.
"""
from __future__ import print_function
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import pycuda.driver as drv
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 matrix multiplication for type ' + str(np.dtype(t)))
if np.iscomplexobj(t()):
a = np.asarray(np.random.rand(10, 5) + 1j * np.random.rand(10, 5), t)
b = np.asarray(np.random.rand(5, 5) + 1j * np.random.rand(5, 5), t)
c = np.asarray(np.random.rand(5, 5) + 1j * np.random.rand(5, 5), t)
else:
a = np.asarray(np.random.rand(10, 5), t)
b = np.asarray(np.random.rand(5, 5), t)
c = np.asarray(np.random.rand(5, 5), t)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
temp_gpu = culinalg.dot(a_gpu, b_gpu)
d_gpu = culinalg.dot(temp_gpu, c_gpu)
temp_gpu.gpudata.free()
del(temp_gpu)
print('Success status: ', np.allclose(np.dot(np.dot(a, b), c), d_gpu.get()))
print('Testing vector multiplication for type ' + str(np.dtype(t)))
if np.iscomplexobj(t()):
d = np.asarray(np.random.rand(5) + 1j * np.random.rand(5), t)
e = np.asarray(np.random.rand(5) + 1j * np.random.rand(5), t)
else:
d = np.asarray(np.random.rand(5), t)
e = np.asarray(np.random.rand(5), t)
d_gpu = gpuarray.to_gpu(d)
e_gpu = gpuarray.to_gpu(e)
temp = culinalg.dot(d_gpu, e_gpu)
print('Success status: ', np.allclose(np.dot(d, e), temp))
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