但是,我得到了奇怪的结果。
看看我画的图。
您可以看到scikit learn的结果,它比theano(GPU)更快。
我检查其经过时间的程序是从一个包含n*40个元素的矩阵计算欧几里德距离矩阵。
points = T.fmatrix("points")
edm = T.zeros_like(points)
def get_point_to_points_euclidean_distances(point_id):
euclideans = (T.sqrt((T.sqr(points- points[point_id, : ])).sum(axis=1)))
return euclideans
def get_EDM_CPU(points):
EDM = np.zeros((points.shape[0], points.shape[0])).astype(np.float32)
for row in range(points.shape[0]):
EDM[row, :] = np.sqrt(np.sum((points - points[row, :])**2, axis=1))
return EDM
def get_sk(points):
EDM = sk.pairwise_distances(a, metric='l2')
return EDM
seq = T.arange(T.shape(points)[0])
(result, _) = theano.scan(fn = get_point_to_points_euclidean_distances, \
outputs_info = None , \
sequences = seq)
get_EDM_GPU = theano.function(inputs = [points], outputs = result, allow_input_downcast = True)
我认为GPU比sci kit learn慢的原因可能是传输时间。所以我用nvprof命令分析了GPU。然后我得到了这个。
==27105== NVPROF is profiling process 27105, command: python ./EDM_test.py
Using gpu device 0: GeForce GTX 580 (CNMeM is disabled, cuDNN not available)
data shape : (10000, 40)
get_EDM_GPU elapsed time : 1.84863090515 (s)
get_EDM_CPU elapsed time : 8.09937691689 (s)
get_EDM_sk elapsed time : 1.10968112946 (s)
ratio : 4.38128395145
==27105== Profiling application: python ./EDM_test.py
==27105== Warning: Found 9 invalid records in the result.
==27105== Warning: This could be because device ran out of memory when profiling.
==27105== Profiling result:
Time(%) Time Calls Avg Min Max Name
71.34% 1.28028s 9998 128.05us 127.65us 128.78us kernel_reduce_01_node_316e2e1cbfbe8cfb8e4a101f329ffeec_0(int, int, float const *, int, int, float*, int)
19.95% 357.97ms 9997 35.807us 35.068us 36.948us kernel_Sub_node_bc41b3f8f12c93d29f2c4360ad445d80_0_2(unsigned int, int, int, float const *, int, int, float const *, int, int, float*, int, int)
7.32% 131.38ms 2 65.690ms 1.2480us 131.38ms [CUDA memcpy DtoH]
1.25% 22.456ms 9996 2.2460us 2.1140us 2.8420us kernel_Sqrt_node_23508f8f49d12f3e8369d543f5620c15_0_Ccontiguous(unsigned int, float const *, float*)
0.12% 2.1847ms 1 2.1847ms 2.1847ms 2.1847ms [CUDA memset]
0.01% 259.73us 5 51.946us 640ns 250.36us [CUDA memcpy HtoD]
0.00% 17.086us 1 17.086us 17.086us 17.086us kernel_reduce_ccontig_node_97496c4d3cf9a06dc4082cc141f918d2_0(unsigned int, float const *, float*)
0.00% 2.0090us 1 2.0090us 2.0090us 2.0090us void copy_kernel<float, int=0>(cublasCopyParams<float>)
{ 1.248 [us], 131.38 [ms] }
传输[CUDA memcpy HtoD]执行5x
{ min: 640 [ns], max: 250.36 [us] }
传输时间约为131.639 ms(131.88 ms+259.73 us)。
