python变量内存地址释放与加速并行计算多线程

1、导入numba和gc包进行并行计算和内存释放

　　代码如下很容易的：

#coding:utf-8
import time

from numba import jit, prange, vectorize
from numba import cuda
from numba import njit
import numpy as np
import gc

def adds(x,y,m):
    return [x*i for i in range(y)]

@jit(parallel=True,nogil=True)
# @njit(parallel=True,nogil=True)
def adds1(x,y,m):
    sd =  np.empty((y))
    for i in prange(y):
        for j in range(m):
            sd[i]=x*i*m
    return sd

@jit(parallel=True,nogil=True)
def test(n):
    temp = np.empty((50, 50)) # <--- allocation is hoisted as a loop invariant as `np.empty` is considered pure
    for i in prange(n):
        temp[:] = 0           # <--- this remains as assignment is a side effect
        for j in range(50):
            temp[j, j] = i
    return temp

if __name__=="__main__":
    n = 50
    max = 10000**2*12
    m=100
    # st1 = time.time()
    # val_1 = adds(n,max,m)
    # print(time.time()-st1)

    st2 = time.time()
    val_2 = adds1(n,max,m)
    print(time.time()-st2)
    # 释放内存地址
    del val_2,n,max,m
    gc.collect()

    st3 = time.time()
    tmp = test(100**3*10)
    print(time.time()-st3)
    # 释放temp的内存地址
    del tmp
    gc.collect()