I recently generated a sparse matrix using function: sparse. When I do the profiling, I found the vast majority of the runtime is spent on calling function sparse, which is pretty shocking to me.
To find out if generating a sparse matrix is slow across all the programming languages. I use scipy.sparse.coo_matrix in python to perform the same task. What suprised me is that scipy.sparse.coo_matrix has 10X speed of that of Matlab's sparse function.
Matlab demo Code:
RowInd = repmat(randperm(262144),81,1);
RowInd = RowInd(1:260100*81) ;
ColInd = repmat(randperm(262144),81,1);
ColInd = ColInd(1:260100*81);
Val = randn(260100*81,1);
tStart = tic;
L=sparse(RowInd,ColInd,Val, 262144, 262144 ,260100*81);
tEnd = toc(tStart);
disp(['Runtime of generating a sparse matrix in Matlab:', num2str(tEnd), ' second.']);
Python demo Code:
import numpy as np
import scipy.sparse
import scipy.sparse.linalg
from time import time
if __name__ == "__main__":
nz_indsRow = np.tile(np.random.permutation(262144), 81)
nz_indsRow = nz_indsRow[:260100*81]
nz_indsCol = np.tile(np.random.permutation(262144), 81)
nz_indsCol = nz_indsCol[:260100*81]
nz_indsVal = np.random.rand(260100*81)
print(nz_indsRow.shape, nz_indsCol.shape, nz_indsVal.shape)
t0 = time()
L = scipy.sparse.coo_matrix(
(nz_indsVal, (nz_indsRow, nz_indsCol)), shape=(262144, 262144))
t1 = time()
print('Runtime of generating a sparse matrix via SicPy:', t1-t0, 'second.')
In my desktop: the runtime is 1.2399 s vs 0.12721 s.
Can someone explain to me that why sparse function in Matlab is so slow? How to find a more efficient function that generate a sparse matrix in Matlab?
