How to define temporal operators as sparse arrays in both numpy (for CPU) and cupy (for GPU)? #1
Description
It is possible to define temporal operators as sparse arrays, and doing this is extremely significant in the case that the temporal dimensionality is very large, e.g., T = 100,000.
- We can first check out the original code for defining temporal operators.
import numpy as np
def generate_Psi(T, d, season):
Psi = []
for k in range(0, d + 1):
if k == 0:
Psi.append(np.append(np.zeros((T - d - season, d)),
np.append(-1 * np.eye(T - d - season), np.zeros((T - d - season, season)), axis = 1)
+ np.append(np.zeros((T - d - season, season)), np.eye(T - d - season), axis = 1), axis = 1))
else:
Psi.append(np.append(np.append(np.zeros((T - d - season, d - k)),
np.append(-1 * np.eye(T - d - season), np.zeros((T - d - season, season)), axis = 1)
+ np.append(np.zeros((T - d - season, season)), np.eye(T - d - season), axis = 1), axis = 1),
np.zeros((T - d - season, k)), axis = 1))
return Psi- For CPU implementation, we can use
numpyandscipy.
import numpy as np
from scipy.sparse import coo_matrix
def generate_Psi(T, d, season):
Psi = []
for k in range(0, d + 1):
if k == 0:
Psi.append(coo_matrix((- np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d, T - season))),
shape = (T - d - season, T)).tocsr()
+ coo_matrix((np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d + season, T))),
shape = (T - d - season, T)).tocsr())
else:
Psi.append(coo_matrix((- np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d - k, T - k - season))),
shape = (T - d - season, T)).tocsr()
+ coo_matrix((np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d - k + season, T - k))),
shape = (T - d - season, T)).tocsr())
return Psi- For GPU implementation, we can use
cupy.
import cupy as np
from cupy.sparse import coo_matrix
def generate_Psi(T, d, season):
Psi = []
for k in range(0, d + 1):
if k == 0:
Psi.append(coo_matrix((- np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d, T - season))),
shape = (T - d - season, T)).tocsr()
+ coo_matrix((np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d + season, T))),
shape = (T - d - season, T)).tocsr())
else:
Psi.append(coo_matrix((- np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d - k, T - k - season))),
shape = (T - d - season, T)).tocsr()
+ coo_matrix((np.ones(T - d - season),
(np.arange(0, T - d - season), np.arange(d - k + season, T - k))),
shape = (T - d - season, T)).tocsr())
return PsiNote that this is not an issue, but this is a really interesting question for improving your Python implementation.