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31 lines
841 B
Python
31 lines
841 B
Python
4 years ago
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import numpy as np
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from pycuda import gpuarray
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from time import time
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# -- initialize the device
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import pycuda.autoinit
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print((gpuarray.to_gpu(np.array([1], dtype=np.float32)) * 1).get()) # this call speeds up the following gpu
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# calculation, because at this point the gpu code gets compiled and cached for the next calls
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# compare cpu and gpu
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host_data = np.float32(np.random.random(50000000))
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t1 = time()
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host_data_2x = host_data * np.float32(2)
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t2 = time()
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print('total time to compute on CPU: %f' % (t2 - t1))
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device_data = gpuarray.to_gpu(host_data)
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t1 = time()
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device_data_2x = device_data * np.float32(2)
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t2 = time()
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from_device = device_data_2x.get()
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print('total time to compute on GPU: %f' % (t2 - t1))
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print('Is the host computation the same as the GPU computation? : {}'.format(np.allclose(from_device, host_data_2x)))
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