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我比较了使用Python的ano(CPU)、theano(GPU)和scikitlearn(CPU)的处理时间。
但是，我得到了奇怪的结果。
看我画的图。在
处理时间比较：
您可以看到scikit learn的结果，它比atano(GPU)更快。
我检查它的运行时间的程序是从一个有n*40个元素的矩阵计算欧几里德距离矩阵。在
这是代码的一部分。在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)
我想GPU比sci工具包学习慢的原因可能是转移时间。所以我用nvprof命令分析了GPU。然后我得到了这个。在
^{pr2}$
传输[CUDA memcpy DtoH]执行了两次{ 1.248 [us], 131.38 [ms] }
转移[CUDA memcpy HtoD]进行了5x{ min: 640 [ns], max: 250.36 [us] }
传输时间约为131.639ms(131.88ms+259.73us)。
但是GPU和scikit learn之间的差距大约是700ms(1.8s-1.1s)，因此，这个差距是在传输时间上的。在
它只计算对称矩阵的上三角矩阵吗？在
是什么让scikit学得这么快？在
