if __name__ == '__main__':
GaussianProcess.set_testing_val = MethodType(set_testing_val, None,
GaussianProcess)
print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
print '-----------------------------'
for Npredict in xrange(np.int(1e5), np.int(1e6), np.int(1e5)):
Ntrain = 250
Ninputs = 10
inputs = np.random.random((Ntrain, Ninputs))
testing = np.random.random((Npredict, Ninputs))
gp = GaussianProcess(inputs, [])
gp.set_testing_val(Ninputs, Npredict, Ntrain)
#CPU predict
start = time.time()
[mu_c, var_c, deriv_c] = gp.predict(testing, is_gpu=False)
end = time.time()
cputime = end - start
#GPU predict
start = time.time()
[mu_g, var_g, deriv_g] = gp.predict(testing,
is_gpu=True,
precision=np.float32,
threshold=1e5)
end = time.time()
gputime = end - start
if __name__ == '__main__':
GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
print '-----------------------------'
for Npredict in xrange(np.int(1e5), np.int(1e6), np.int(1e5)):
Ntrain = 250
Ninputs = 10
inputs = np.random.random(( Ntrain, Ninputs))
testing = np.random.random(( Npredict, Ninputs))
gp = GaussianProcess(inputs, [])
gp.set_testing_val(Ninputs, Npredict, Ntrain)
#CPU predict
start = time.time()
[mu_c, var_c, deriv_c] = gp.predict(testing, is_gpu=False )
end = time.time()
cputime = end -start
#GPU predict
start = time.time()
[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float32, threshold = 1e5)
end =time.time()
gputime = end - start
print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (Npredict, cputime, gputime, cputime/gputime),
