1、導入numba和gc包進行并行計算和內存釋放

代碼如下很容易的：

#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)

# 釋放內存地址

del val_2,n,max,m

gc.collect()

st3 = time.time()

tmp = test(100**3*10)

print(time.time()-st3)

# 釋放temp的內存地址

del tmp

gc.collect()

總結

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