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38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
import numpy as np
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import pycuda.autoinit
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from pycuda import gpuarray
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from time import time
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from pycuda.elementwise import ElementwiseKernel
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host_data = np.float32(np.random.random(50000000))
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gpu_2x_ker = ElementwiseKernel(
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"float *in, float *out",
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"out[i] = 2*in[i];",
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"gpu_2x_ker")
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# warm up
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test_data = gpuarray.to_gpu(host_data)
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gpu_2x_ker(test_data, gpuarray.empty_like(test_data))
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def speed_comparison():
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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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# allocate memory for output
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device_data_2x = gpuarray.empty_like(device_data)
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t1 = time()
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gpu_2x_ker(device_data, device_data_2x)
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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(
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'Is the host computation the same as the GPU computation? : {}'.format(np.allclose(from_device, host_data_2x)))
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if __name__ == '__main__':
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speed_comparison()
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