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使用numba对Python运算加速的方法

有时候需要比较大的计算量,这个时候Python的效率就很让人捉急了,此时可以考虑使用numba 进行加速,效果提升明显~

(numba 安装貌似很是繁琐,建议安装Anaconda,里面自带安装好各种常用科学计算库)

from numba import jit

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

测试效果:

(关于__wrapped__ 见我的博文: 浅谈解除装饰器作用(python3新增) )

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

可以看到使用jit 加速后,即使设置测试一次,实际上还是取了三次的最优值,如果取最坏值(因为最优值可能是缓存下来的),则耗时为395ns * 13 大概是5us 还是比不使用的52.9us 快上大概10倍,

增大计算量可以看到使用numba加速后的效果提升更加明显,

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

如果减少计算量,可以看到当降到明显小值时,使用加速后的效果(以最差计)与不加速效果差距不大,因此如果涉及到较大计算量不妨使用jit 加速下,何况使用起来这么简便。

%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

以上这篇使用numba对Python运算加速的方法就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。