CODEWITHSUNDEEP
pythonperformancematrixlinear-algebracython
Daniel Thaagaard Andreasen
Daniel Thaagaard Andreasen

Reputation: 8556

An efficient way to create a matrix with cython

I have a function that calculates a matrix for me but it is really slow. Even in cython it is running slow, so I was wondering if one could do anything to enhance the below code.

EDIT: I've changed or added

des = np.zeros([n-m+1,m]) to cdef np.ndarray des = np.zeros([n-m+1,m], dtype=DTYPE) (This is faster than np.empty... Instead of saying m/2 I've added a cdef int m2 = m/2 but that didn't seemed to help anything.

cimport numpy as np
cimport cython

DTYPE = float
ctypedef np.float_t DTYPE_t

@cython.boundscheck(False)
@cython.cdivision(True)
@cython.wraparound(False)
cpdef map4(np.ndarray[DTYPE_t, ndim=1] s, int m): 

  cdef int n = len(s)
  cdef int i
  cdef int j

  des = np.zeros([n-m+1,m])
  for j in xrange(m):
      for i in xrange(m/2,n-m/2-1):
          des[i-m/2,j] = s[i-j+m/2]

  return des, s, m, n

Typically n~10000 and m=1001.

Upvotes: 2

Views: 3062

Answers (3)

Max
Max

Reputation: 121

I'm not seeing m being set anywhere. At the bottom of your code, you mention that n~10,000, and m=1001. Does that mean that m is a constant integer of 32 bits? Not seeing your compilation flags, it's frequently worthwhile to try it with and without -ffast-math to see if that makes a difference. With large arrays and matrices, using a smaller data type usually shows a significant speedup, provided that the smaller data type preserves the range and accuracy that your program needs, though I'm not seeing a large potential benefit on this calculation.

If you could show us the C code that is generated by this, that might help, as well.

Upvotes: 0

askewchan
askewchan

Reputation: 46578

It might also help to use np.empty instead of np.zeros, assuming you'll assign each element:

des = np.empty([n-m+1,m])

Upvotes: 2

Justin Peel
Justin Peel

Reputation: 47092

Try:

cdef np.ndarray des = np.zeros([n-m+1,m])

You can also make this more specific like you did for the parameter s. You can also turn off bounds checking. Check out the cython numpy tutorial.

You also might want to make a variable:

cdef int m_2 = m/2

and use that everywhere you have m/2 because I don't know if Cython will do that optimization for you.

Upvotes: 3

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