最近工作中有个任务，就是优化一个模型的实时性。从有到无，主要完成了以下内容。
0.模型的逻辑
1.算法逻辑
2.代码重构
3.程序的性能优化，包括编译、多线程、多进程、numba
4.语言
numba包，经测试，比较适用于数组、矩阵等数值计算，其他的类型操作，容易报错。
from multiprocessing import Pool
from functools import partial
def math_get(a, b):
print(a)
return b + a
def processing(b):
pool = Pool(3)
a = [i for i in range(3)]
d = pool.map(partial(math_get,b = b), a,)
#多个参数代入的时候，采用partial的方式，
#其中a作为一个可迭代的对象通过map代入math_get函数
print(d)
pool.close()
pool.join()
if __name__ == "__main__":
processing(5)
#!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = 'Great'
from multiprocessing import Pool
import time
def a(num):
list_num = []
print(num)
for i in num:
list_num.append(i)
print(i)
print(list_num)
return list_num
if __name__ == "__main__":
start = time.time()
pool = Pool(3)#进程池
num = [1,2,3,4,5,6]
#pool.apply_async(a,args=(num,))
a = pool.map(a,(num, ))
print('a',a)
pool.close()
pool.join()
end = time.time()
print(end-start)#!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = 'Great'
from multiprocessing import Pool
import time
from functools import partial
def a(num, ccc):
list_num = []
for i in num:
#list_num.append(i)
print(i + ccc)
#print(list_num)
#return list_num
if __name__ == "__main__":
start = time.time()
pool = Pool(3)
num = [1,2,3,4,5,6]
ccc = 1
#for i in range(10):
#pool.apply_async(a,args=(num,))
pool.map(partial(a,ccc = ccc),(num,))#多个参数
#print('a',a)
pool.close()
pool.join()
end = time.time()
print(end-start)