parallelizing python finite element analysis

Question

I am trying to parallelize the python function below, which is involved in finite element analysis. In particular, I am trying to make the for loop in the function runs in parallel. I have done it in Matlab using parfor, and I am trying to do the same in python.

def assemble(u):
K=np.zeros((ndof,ndof)) # initializing global stiffness matrix
Fint=np.zeros(ndof)
Fext=np.zeros(ndof)
for iel in range(tne):
    elnodes=elems[iel,:] # nodes of the local elements
    xcel=nodes[elnodes,0] # x-coordinates for the local elements
    ycel=nodes[elnodes,1] # y-coordinates for the local elements
    zcel=nodes[elnodes,2] # z-coordinates for the local elements
    dof=np.array([3*elnodes[0],3*elnodes[0]+1,3*elnodes[0]+2,3*elnodes[1],\
                  3*elnodes[1]+1,3*elnodes[1]+2,3*elnodes[2],3*elnodes[2]+1,\
                  3*elnodes[2]+2,3*elnodes[3],3*elnodes[3]+1,3*elnodes[3]+2,\
                  3*elnodes[4],3*elnodes[4]+1,3*elnodes[4]+2,3*elnodes[5],\
                  3*elnodes[5]+1,3*elnodes[5]+2,3*elnodes[6],3*elnodes[6]+1,\
                  3*elnodes[6]+2,3*elnodes[7],3*elnodes[7]+1,3*elnodes[7]+2]).flatten()
    u_el=u[dof]
    strain,stress=SS(xcel,ycel,zcel,u_el)
    ESM,Fint_e,Fext_e=Elem_KF(xcel,ycel,zcel,strain,stress)
    K[np.ix_(dof,dof)]+=ESM
    Fint[dof]+=Fint_e
    Fext[dof]+=Fext_e
R=Fext-Fint
return K,Fint,Fext,R

I have tried this, but it did not work for me:

def assemble(u):
dof2    = np.zeros((tne,24))
Fint_e2 = np.zeros((tne,24))
Fext_e2 = np.zeros((tne,24))
ppp = Pool(2)
for iel in range(tne):
    Fint_e2[iel,:], Fext_e2[iel,:], dof2[iel,:] = ppp.apply(workers,args=(iel,u,))
ppp.close()
ppp.join()
return Fint_e2, Fext_e2, dof2

def workers(iel, u):
print(iel)
elnodes=elems[iel,:].long() # nodes of the local elements
xcel=nodes[elnodes,0] # x-coordinates for the local elements
ycel=nodes[elnodes,1] # y-coordinates for the local elements
zcel=nodes[elnodes,2] # z-coordinates for the local elements
dof=np.array([3*elnodes[0],3*elnodes[0]+1,3*elnodes[0]+2,\
                  3*elnodes[1],3*elnodes[1]+1,3*elnodes[1]+2,\
                  3*elnodes[2],3*elnodes[2]+1,3*elnodes[2]+2,\
                  3*elnodes[3],3*elnodes[3]+1,3*elnodes[3]+2,\
                  3*elnodes[4],3*elnodes[4]+1,3*elnodes[4]+2,\
                  3*elnodes[5],3*elnodes[5]+1,3*elnodes[5]+2,\
                  3*elnodes[6],3*elnodes[6]+1,3*elnodes[6]+2,\
                  3*elnodes[7],3*elnodes[7]+1,3*elnodes[7]+2]).flatten()
u_el=u[dof]
strain,stress = Stress_Strain(xcel,ycel,zcel,u_el)
Fint_e,Fext_e = KEL(xcel,ycel,zcel,strain,stress)
return Fint_e, Fext_e, dof

Any help would be highly appreciated. Thanks!

Does this answer your question? stackoverflow.com/questions/4682429/parfor-for-python There are some nice answers there. — seanpue
– seanpue, Commented May 6, 2020 at 0:38
loops in python are not as fast as in matlab - much slower actually. You will have a better time trying to vectorize your code (if at all possible) instead of trying to get it to run fast with multiprocessing or concurrent.futures - because you will pay for the performance hit of pickling across processes and get no CPU-bound speed improvements with threads because of the GIL — modesitt
– modesitt, Commented May 6, 2020 at 1:42

João Ramiro · Accepted Answer · 2020-05-06 01:41:30Z

1

You can use Pools, to use multiple processes at the same time.

from multiprocessing import Pool

p = Pool(n_threads)

for iel in range(tne):
   #function will calculate all the parameters you have inside the loop
   results = p.apply(function,args=(x,y,z,etc,))  

#wait for all threads to return their value
p.close()
p.join()

edited May 6, 2020 at 1:41

answered May 6, 2020 at 0:51

João Ramiro

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1 Comment

Abu2Stack Over a year ago

Hi Ramiro, Thanks for the reply. I tried something along the lines you suggested, but it did not work for me. Please see my answer below.

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