I'm currently making some numerical solver for current simulation. To make my code faster, I made a function that returns the result of elementwise matrix multiplication, and gradient... and so on.
def mmul(A, B, procname, return_dict):
return_dict[procname] = np.multiply(A,B)
def mgrad(A, procname, return_dict):
return_dict[procname] = np.gradient(A/dx)
def madd(A, B, procname, return_dict):
return_dict[procname] = A+B
Now here's the body of the code. I first made a dictionary(return_dict) and store the results for each processing units, and get the values(Vgrad, Pgrad, Psquare) from the dictionary.
for k in range(0,max_iter-1, 1):
#0. Firstly generate all of the auxiliay calculation arrays
post_V, post_p, Vij_coeff = np.zeros((3, lx, ly), dtype = float)
# Calculate carrier density of the next step
processes = []
#---------------------------- # Const/mtx for calculating p
manager = multiprocessing.Manager()
return_dict = manager.dict()
p0 = multiprocessing.Process(target = mgrad, args = (V, Vgrad, return_dict))
processes.append(p0)
p0.start()
p1 = multiprocessing.Process(target = mgrad, args = (p, Pgrad, return_dict))
processes.append(p1);p1.start()
p2 = multiprocessing.Process(target = mmul, args = (p,p, Psquare, return_dict))
processes.append(p2);p2.start()
for process in processes:
process.join()
Vgrad = return_dict['Vgrad']
Pgrad = return_dict['Pgrad']
Psquare = return_dict['Psquare']
However, this code makes the error below
PicklingError: Can't pickle <function mgrad at 0x000002776C3614C8>: it's not the same object as __main__.mgrad
Is there any solutions to get the calculated value of the function, while running in multiprocessor?
question from:
https://stackoverflow.com/questions/65878819/how-to-get-return-value-of-different-functions-in-a-for-loop-with-multiprocessin 与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…