I'm trying to optimize the performance of some numpy code. The problem is, I'm just blindly replacing code with equivalent code, and profiling to compare the results.
Does numpy provide any resource describing the complexity/performance of its basic operations (either a theoretical analysis, or some practical rules of the thumb), for example
- basic operations (eg: sum, multiplication, exponentiation)
- different data types (eg: float vs complex)
- vectorization/broadcasting as function of the size of the matrix (eg: vectorizing beyond 2 dimensions seems to make performance worse than equivalent for-loop)