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<p>我对Python非常陌生,对多处理也完全陌生,我在网上找到了一些教程来帮助我理解多处理软件包</p>
<p>我的代码有一组使用RungeKutta4方法的微分方程,我需要在不同的起始条件下运行大量的计算</p>
<p>代码本身可以工作,但没有多处理,需要很长时间才能完成,我认为使用并行化可能是理想的,因为计算是独立的</p>
<p>我正在使用Python作为IDE顺便说一句</p>
<p>所以我用</p>
<pre><code>import multiprocessing as mp
iterations = np.arange(start,end,step)
pool = mp.Pool(mp.cpu_count()) # Step 1: Init multiprocessing.Pool()
results = pool.map(rungeKutta4, [k for k in iterations]) # Step 2: apply pool map
pool.close() # Step 3: close
</code></pre>
<p>当我在Anaconda中运行它时,我没有得到一个错误,它开始计算,但从未停止。。。。
我哪里出错了</p>
<p>提前谢谢你的帮助</p>
<p>最好的</p>
<p>编辑:我在这里添加了全部代码</p>
<pre><code># Python program to implement Runge Kutta method
# Markus Schmid
# 2020 Appalachian State University
# jupyter nbconvert --to python FILENAME.ipynb
# y" + 2*beta*y' + w0*sin(y) = A + B*cos(w*t)
# Import used libraries
import numpy as np
import math
import matplotlib.pyplot as plt
import time
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
import multiprocessing as mp
print("Number of processors: ", mp.cpu_count())
# list for time (x axis) and result (y axis)
result = []
w0 = 10 # undamped angular frequency of the oscillator
beta = 1
B = 1
A = 0
B = 10
w = 4
theta_init = 0
theta_end = 20
theta_step = 0.1
# initial conditions
t0 = 0
y0 = 0
z0 = 0
t_final = 20 # final time
h = 0.01 # fixed step size
n = (int)((t_final - t0)/h) # datapoints per RK4 iteration
# define functions
def funcf(t, y, z):
return (z)
def funcg(t, y, z):
return (A+B*math.cos(w*t) - 2*beta*z - w0*math.sin(y))
# Finds value of y for a given x using step size h
# and initial value y0 at x0.
def rungeKutta4(y):
# Count number of iterations using step size or
# step height h
t = t0
z = z0
n = (int)((t_final - t)/h)
for i in range(1, n + 1):
# Apply Runge Kutta to find next value of y
k1 = h * funcf(t, y, z)
l1 = h * funcg(t, y, z)
k2 = h * funcf(t + 0.5 * h, y + 0.5 * k1, z + 0.5 * l1)
l2 = h * funcg(t + 0.5 * h, y + 0.5 * k1, z + 0.5 * l1)
k3 = h * funcf(t + 0.5 * h, y + 0.5 * k2, z + 0.5 * l2)
l3 = h * funcg(t + 0.5 * h, y + 0.5 * k2, z + 0.5 * l2)
k4 = h * funcf(t + h, y + k3, z + l3)
l4 = h * funcg(t + h, y + k3, z + l3)
# Update next value of y
y = y + (1.0 / 6.0)*(k1 + 2 * k2 + 2 * k3 + k4)
z = z + (1.0 / 6.0)*(l1 + 2 * l2 + 2 * l3 + l4)
#result.append(y)
t = t + h # Update next value of t
return y
iterations = np.arange(theta_init,theta_end+theta_step,theta_step) # number iterations for omega sweep
start_time = time.time()
#for k in iterations: # for serial calculation
# rungeKutta4(k)
pool = mp.Pool(mp.cpu_count()) # Step 1: Init multiprocessing.Pool()
results = pool.map(rungeKutta4, [k for k in iterations]) # Step 2: apply pool map
end_time = time.time()
pool.close() # Step 3: close
print ("The program took", end_time - start_time, "s to run")
#table = np.array(result).reshape(len(iterations),n) # rearrange array, 1 row is const. theta0
timer = np.arange(t0,t_final,h) # time array
</code></pre>