Numpy vs Cython speed

Question

I have an analysis code that does some heavy numerical operations using numpy. Just for curiosity, tried to compile it with cython with little changes and then I rewrote it

Answer 1

As mentioned in the other answers, version 2 is essentially the same as version 1 since cython is unable to dig into the array access operator in order to optimise it. There are 2 reasons for this

• First, there is a certain amount of overhead in each call to a numpy function, as compared to optimised C code. However this overhead will become less significant if each operation deals with large arrays

• Second, there is the creation of intermediate arrays. This is clearer if you consider a more complex operation such as out[row, :] = A[row, :] + B[row, :]*C[row, :]. In this case a whole array B*C must be created in memory, then added to A. This means that the CPU cache is being thrashed, as data is being read from and written to memory rather than being kept in the CPU and used straight away. Importantly, this problem becomes worse if you are dealing with large arrays.

Particularly since you state that your real code is more complex than your example, and it shows a much greater speedup, I suspect that the second reason is likely to be the main factor in your case.

As an aside, if your calculations are sufficiently simple, you can overcome this effect by using numexpr, although of course cython is useful in many more situations so it may be the better approach for you.