torch’s matrix multiplication speed is increasing as the number of multiplications increases for small matrices

```
def timer(func, reps, *args, **kwargs):
s = perf_counter()
for i in range(reps):
func(*args, **kwargs)
e = perf_counter()
return e - s
y = list(range(16))
t = torch.Tensor(y * 16).reshape((16, 16))
u = torch.Tensor(y)
v = t.numpy()
w = u.numpy()
n = 8
time_torch_cpu = [timer(torch.matmul, 10**i, t, u) for i in range(1, n)]
time_numpy = [timer(np.dot, 10**i, v, w) for i in range(1, n)]
x = [i for i in range(1, n)]
plt.plot(x, time_numpy, color='red', label='numpy')
plt.plot(x, time_torch_cpu, color='blue', label='pytorch cpu')
plt.ylabel('time (seconds)')
plt.xlabel('No: of multiplications (10^i)')
plt.legend()
plt.show()
```