Scipy sparse parallel iterative solvers
I need to solve quite large linear systems (about 1e6 x 1e6, maybe more in future) in python. The matrix is stored in csr format.
I tried scipy.sparse.linalg.spsolve, which is a direct solver and is not applicable due to high RAM consumption.
I had a look at the iterative solvers scipy.sparse.linalg.bicgstab and scipy.sparse.linalg.gmres.
Are these capable of parallel computing? When I use small dimensions, only one core is used, when I use larger dimension (1e6) all 4 cores are used.
Could somebody explain the difference?
Here's a small sample code I use:
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as sp_la
n = int(1e6)
A = sp.spdiags(np.random.rand(3, n), [-1, 0, 1], n, n)
b = np.random.rand(n, 1)
x = sp_la.gmres(A, b)
I want to run the code on a cluster with 48 cpus and right now I am trying the code on my laptop (4 cores).
Thanks
python parallel-processing scipy iteration solver
asked Nov 25 '18 at 15:04
SebastianSebastian
11
add a comment |
I need to solve quite large linear systems (about 1e6 x 1e6, maybe more in future) in python. The matrix is stored in csr format.
I tried scipy.sparse.linalg.spsolve, which is a direct solver and is not applicable due to high RAM consumption.
I had a look at the iterative solvers scipy.sparse.linalg.bicgstab and scipy.sparse.linalg.gmres.
Are these capable of parallel computing? When I use small dimensions, only one core is used, when I use larger dimension (1e6) all 4 cores are used.
Could somebody explain the difference?
Here's a small sample code I use:
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as sp_la
n = int(1e6)
A = sp.spdiags(np.random.rand(3, n), [-1, 0, 1], n, n)
b = np.random.rand(n, 1)
x = sp_la.gmres(A, b)
I want to run the code on a cluster with 48 cpus and right now I am trying the code on my laptop (4 cores).
Thanks
python parallel-processing scipy iteration solver
asked Nov 25 '18 at 15:04
SebastianSebastian
11
add a comment |
I need to solve quite large linear systems (about 1e6 x 1e6, maybe more in future) in python. The matrix is stored in csr format.
I tried scipy.sparse.linalg.spsolve, which is a direct solver and is not applicable due to high RAM consumption.
I had a look at the iterative solvers scipy.sparse.linalg.bicgstab and scipy.sparse.linalg.gmres.
Are these capable of parallel computing? When I use small dimensions, only one core is used, when I use larger dimension (1e6) all 4 cores are used.
Could somebody explain the difference?
Here's a small sample code I use:
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as sp_la
n = int(1e6)
A = sp.spdiags(np.random.rand(3, n), [-1, 0, 1], n, n)
b = np.random.rand(n, 1)
x = sp_la.gmres(A, b)
I want to run the code on a cluster with 48 cpus and right now I am trying the code on my laptop (4 cores).
Thanks
python parallel-processing scipy iteration solver
asked Nov 25 '18 at 15:04
SebastianSebastian
11
I need to solve quite large linear systems (about 1e6 x 1e6, maybe more in future) in python. The matrix is stored in csr format.
I tried scipy.sparse.linalg.spsolve, which is a direct solver and is not applicable due to high RAM consumption.
I had a look at the iterative solvers scipy.sparse.linalg.bicgstab and scipy.sparse.linalg.gmres.
Are these capable of parallel computing? When I use small dimensions, only one core is used, when I use larger dimension (1e6) all 4 cores are used.
Could somebody explain the difference?
Here's a small sample code I use:
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as sp_la
n = int(1e6)
A = sp.spdiags(np.random.rand(3, n), [-1, 0, 1], n, n)
b = np.random.rand(n, 1)
x = sp_la.gmres(A, b)
I want to run the code on a cluster with 48 cpus and right now I am trying the code on my laptop (4 cores).
Thanks
python parallel-processing scipy iteration solver
python parallel-processing scipy iteration solver
asked Nov 25 '18 at 15:04
SebastianSebastian
11
asked Nov 25 '18 at 15:04
SebastianSebastian
11
asked Nov 25 '18 at 15:04
SebastianSebastian
11
asked Nov 25 '18 at 15:04
SebastianSebastian
11
asked Nov 25 '18 at 15:04
SebastianSebastian
11
11
add a comment |
add a comment |
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