How to share two numpy arrays as shared memory and make execution faster
I have to run least-square on each row of a numpy array. I am using sciki-learn joblib module for parallel processing. But unfortunately I am not getting the performance gain I am hoping for. I think, it might be because of static matrices being copied again and again. Below is my code snippet. How can I make it faster ?
A sample code (this is just an example). My arrays are of size 50k*10k
from sklearn.externals.joblib import Parallel,delayed
from numpy.linalg import norm,lstsq
V = np.random.rand(5,5)
W = np.random.randint(0,2,(5,5))
for i in xrange(1,max_iter+1):
U = Parallel(n_jobs=-1)(delayed(lstsq)(np.dot(V,np.diag(Wu)), np.dot(V[u],np.diag(Wu)),rcond=None) for u,Wu in enumerate(W))
Both V and W remains constant for each row operation. I was hoping that sklearn automatically makes it a shared object between parallel threads/process.
python-2.7 parallel-processing scikit-learn
asked Nov 21 '18 at 8:44
ShewShew
5101516
add a comment |
I have to run least-square on each row of a numpy array. I am using sciki-learn joblib module for parallel processing. But unfortunately I am not getting the performance gain I am hoping for. I think, it might be because of static matrices being copied again and again. Below is my code snippet. How can I make it faster ?
A sample code (this is just an example). My arrays are of size 50k*10k
from sklearn.externals.joblib import Parallel,delayed
from numpy.linalg import norm,lstsq
V = np.random.rand(5,5)
W = np.random.randint(0,2,(5,5))
for i in xrange(1,max_iter+1):
U = Parallel(n_jobs=-1)(delayed(lstsq)(np.dot(V,np.diag(Wu)), np.dot(V[u],np.diag(Wu)),rcond=None) for u,Wu in enumerate(W))
Both V and W remains constant for each row operation. I was hoping that sklearn automatically makes it a shared object between parallel threads/process.
python-2.7 parallel-processing scikit-learn
asked Nov 21 '18 at 8:44
ShewShew
5101516
add a comment |
I have to run least-square on each row of a numpy array. I am using sciki-learn joblib module for parallel processing. But unfortunately I am not getting the performance gain I am hoping for. I think, it might be because of static matrices being copied again and again. Below is my code snippet. How can I make it faster ?
A sample code (this is just an example). My arrays are of size 50k*10k
from sklearn.externals.joblib import Parallel,delayed
from numpy.linalg import norm,lstsq
V = np.random.rand(5,5)
W = np.random.randint(0,2,(5,5))
for i in xrange(1,max_iter+1):
U = Parallel(n_jobs=-1)(delayed(lstsq)(np.dot(V,np.diag(Wu)), np.dot(V[u],np.diag(Wu)),rcond=None) for u,Wu in enumerate(W))
Both V and W remains constant for each row operation. I was hoping that sklearn automatically makes it a shared object between parallel threads/process.
python-2.7 parallel-processing scikit-learn
asked Nov 21 '18 at 8:44
ShewShew
5101516
I have to run least-square on each row of a numpy array. I am using sciki-learn joblib module for parallel processing. But unfortunately I am not getting the performance gain I am hoping for. I think, it might be because of static matrices being copied again and again. Below is my code snippet. How can I make it faster ?
A sample code (this is just an example). My arrays are of size 50k*10k
from sklearn.externals.joblib import Parallel,delayed
from numpy.linalg import norm,lstsq
V = np.random.rand(5,5)
W = np.random.randint(0,2,(5,5))
for i in xrange(1,max_iter+1):
U = Parallel(n_jobs=-1)(delayed(lstsq)(np.dot(V,np.diag(Wu)), np.dot(V[u],np.diag(Wu)),rcond=None) for u,Wu in enumerate(W))
Both V and W remains constant for each row operation. I was hoping that sklearn automatically makes it a shared object between parallel threads/process.
python-2.7 parallel-processing scikit-learn
python-2.7 parallel-processing scikit-learn
asked Nov 21 '18 at 8:44
ShewShew
5101516
asked Nov 21 '18 at 8:44
ShewShew
5101516
asked Nov 21 '18 at 8:44
ShewShew
5101516
asked Nov 21 '18 at 8:44
ShewShew
5101516
asked Nov 21 '18 at 8:44
ShewShew
5101516
5101516
add a comment |
add a comment |
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