使用numba对Python运算加速的方法

from numba import jit

@jit
def t(count=1000):
 total = 0
 for i in range(int(count)):
  total += i
 return total

In [17]: %timeit -n 1 t.__wrapped__()
1 loop, best of 3: 52.9 µs per loop

In [18]: %timeit -n 1 t()
The slowest run took 13.00 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 395 ns per loop

In [19]: %timeit -n 10 t.__wrapped__(1e6)
10 loops, best of 3: 76.2 ms per loop

In [20]: %timeit -n 1 t(1e6)
The slowest run took 8.00 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 790 ns per loop

%timeit -n 1 t(10)
1 loop, best of 3: 0 ns per loop

%timeit -n 100 t.__wrapped__(10)
100 loops, best of 3: 1.79 µs per loop

%timeit -n 1 t(1)
The slowest run took 17.00 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 395 ns per loop

%timeit -n 100 t.__wrapped__(1)
100 loops, best of 3: 671 ns per loop