Parallellizing Intial-boundary value problem (Finite difference)
I am running a simulation to solve the advection diffusion equation. I wish to parallelize the part of the code where I calculate the partial derivatives so as to speed up my computation. Here is what I am doing:
p1 = np.zeros((len(r), len(th)-1 )) #The solution of the matrix
def func(i):
pti = np.zeros(len(th)-1)
for j in range (len(pti)):
abc = f(p1) #Some function calculating the derivatives at each point
pti[j] = p1[i][j] + dt*( abc ) #dt is some small float number
return pti
#Setting the initial condition of the p1 matrix
for i in range(len(p1[:,0])):
for j in range(len(p1[0])):
p1[i][j] = 0.01
#Final loop calculating the integral by finite difference scheme
p = Pool(args.cores)
for k in range (0,args.iterations): #This is integration in time
p1=p.map(func,range(len(r)))
print (p1)
The problem that I am facing here is that my p1 matrix is not updating after each iteration in k. In the end when I print p1 I get the same matrix that I initialized.
Also, the linear version of this code is working (but it takes too long).
python-3.x parallel-processing nested-loops
edited Nov 11 at 13:00
Zoe
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
add a comment |
I am running a simulation to solve the advection diffusion equation. I wish to parallelize the part of the code where I calculate the partial derivatives so as to speed up my computation. Here is what I am doing:
p1 = np.zeros((len(r), len(th)-1 )) #The solution of the matrix
def func(i):
pti = np.zeros(len(th)-1)
for j in range (len(pti)):
abc = f(p1) #Some function calculating the derivatives at each point
pti[j] = p1[i][j] + dt*( abc ) #dt is some small float number
return pti
#Setting the initial condition of the p1 matrix
for i in range(len(p1[:,0])):
for j in range(len(p1[0])):
p1[i][j] = 0.01
#Final loop calculating the integral by finite difference scheme
p = Pool(args.cores)
for k in range (0,args.iterations): #This is integration in time
p1=p.map(func,range(len(r)))
print (p1)
The problem that I am facing here is that my p1 matrix is not updating after each iteration in k. In the end when I print p1 I get the same matrix that I initialized.
Also, the linear version of this code is working (but it takes too long).
python-3.x parallel-processing nested-loops
edited Nov 11 at 13:00
Zoe
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
It looks like you want some synchronization happening between the iterations? If that isProcessPoolfrommultiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).
– Martin Ueding
Nov 11 at 13:04
add a comment |
I am running a simulation to solve the advection diffusion equation. I wish to parallelize the part of the code where I calculate the partial derivatives so as to speed up my computation. Here is what I am doing:
p1 = np.zeros((len(r), len(th)-1 )) #The solution of the matrix
def func(i):
pti = np.zeros(len(th)-1)
for j in range (len(pti)):
abc = f(p1) #Some function calculating the derivatives at each point
pti[j] = p1[i][j] + dt*( abc ) #dt is some small float number
return pti
#Setting the initial condition of the p1 matrix
for i in range(len(p1[:,0])):
for j in range(len(p1[0])):
p1[i][j] = 0.01
#Final loop calculating the integral by finite difference scheme
p = Pool(args.cores)
for k in range (0,args.iterations): #This is integration in time
p1=p.map(func,range(len(r)))
print (p1)
The problem that I am facing here is that my p1 matrix is not updating after each iteration in k. In the end when I print p1 I get the same matrix that I initialized.
Also, the linear version of this code is working (but it takes too long).
python-3.x parallel-processing nested-loops
edited Nov 11 at 13:00
Zoe
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
I am running a simulation to solve the advection diffusion equation. I wish to parallelize the part of the code where I calculate the partial derivatives so as to speed up my computation. Here is what I am doing:
p1 = np.zeros((len(r), len(th)-1 )) #The solution of the matrix
def func(i):
pti = np.zeros(len(th)-1)
for j in range (len(pti)):
abc = f(p1) #Some function calculating the derivatives at each point
pti[j] = p1[i][j] + dt*( abc ) #dt is some small float number
return pti
#Setting the initial condition of the p1 matrix
for i in range(len(p1[:,0])):
for j in range(len(p1[0])):
p1[i][j] = 0.01
#Final loop calculating the integral by finite difference scheme
p = Pool(args.cores)
for k in range (0,args.iterations): #This is integration in time
p1=p.map(func,range(len(r)))
print (p1)
The problem that I am facing here is that my p1 matrix is not updating after each iteration in k. In the end when I print p1 I get the same matrix that I initialized.
Also, the linear version of this code is working (but it takes too long).
python-3.x parallel-processing nested-loops
python-3.x parallel-processing nested-loops
edited Nov 11 at 13:00
Zoe
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
edited Nov 11 at 13:00
Zoe
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
edited Nov 11 at 13:00
Zoe
11k73675
edited Nov 11 at 13:00
Zoe
11k73675
edited Nov 11 at 13:00
Zoe
11k73675
11k73675
asked Nov 4 at 13:14
Prakhar Godara
112
asked Nov 4 at 13:14
Prakhar Godara
112
asked Nov 4 at 13:14
Prakhar Godara
112
112
It looks like you want some synchronization happening between the iterations? If that isProcessPoolfrommultiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).
– Martin Ueding
Nov 11 at 13:04
add a comment |
It looks like you want some synchronization happening between the iterations? If that isProcessPoolfrommultiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).
– Martin Ueding
Nov 11 at 13:04
It looks like you want some synchronization happening between the iterations? If that is
ProcessPool from multiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).– Martin Ueding
Nov 11 at 13:04
It looks like you want some synchronization happening between the iterations? If that is
ProcessPool from multiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).– Martin Ueding
Nov 11 at 13:04
add a comment |
1 Answer
1
active
oldest
votes
Okay I solved this myself. Apparently putting the line
p = Pool(args.cores)
inside the loop
for k in range (0,args.iterations):
does the trick.
answered Nov 7 at 13:10
Prakhar Godara
112
add a comment |
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Okay I solved this myself. Apparently putting the line
p = Pool(args.cores)
inside the loop
for k in range (0,args.iterations):
does the trick.
answered Nov 7 at 13:10
Prakhar Godara
112
add a comment |
Okay I solved this myself. Apparently putting the line
p = Pool(args.cores)
inside the loop
for k in range (0,args.iterations):
does the trick.
answered Nov 7 at 13:10
Prakhar Godara
112
add a comment |
Okay I solved this myself. Apparently putting the line
p = Pool(args.cores)
inside the loop
for k in range (0,args.iterations):
does the trick.
answered Nov 7 at 13:10
Prakhar Godara
112
Okay I solved this myself. Apparently putting the line
p = Pool(args.cores)
inside the loop
for k in range (0,args.iterations):
does the trick.
answered Nov 7 at 13:10
Prakhar Godara
112
answered Nov 7 at 13:10
Prakhar Godara
112
answered Nov 7 at 13:10
Prakhar Godara
112
answered Nov 7 at 13:10
Prakhar Godara
112
112
add a comment |
add a comment |
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It looks like you want some synchronization happening between the iterations? If that is
ProcessPoolfrommultiprocessing, you probably want to use the context manager (with Pool(args.cores) as p:).– Martin Ueding
Nov 11 at 13:04