为什么同样的矩阵乘法， Pytorch 和 Cupy 要比 Numpy 慢？

import cupy as cp
import numpy as np

x_cpu = np.random.randn(100, 100).astype(np.float32)
x_gpu = cp.asarray(x_cpu)

%%timeit
x_cpu*x_cpu

2.41 µs ± 19.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%%timeit
x_gpu*x_gpu

14.3 µs ± 53.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

import torch

x_tensor = torch.from_numpy(x_cpu)

%%timeit
x_tensor*x_tensor

3.01 µs ± 33.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

x_gpu_tensor = x_tensor.cuda()

%%timeit
x_gpu_tensor*x_gpu_tensor

7.85 µs ± 13.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)