Here are the timings I got from the updated version in the attachment. I
think this gives the correct picture:

D:\memview-benchmarks\new>python runme.py
numpy_only: 6.86 sec
cythonized_numpy: 5.74 sec
cythonized_numpy_2: 10.4 sec
cythonized_numpy_2b: 6.25 sec
cythonized_numpy_3: 2.43 sec
cythonized_numpy_4: 1.78 sec
pointer_arith: 1.79 sec
memview: 1.86 sec

There is a table in the attached PDF that should be easier to read.

The overhead from the numpy versions comes from slicing the ndarray. In
comparison, slicing the memoryview has a very small overhead. If we
slice the ndarray in Cython, this is not much better than just using
plain numpy in Python. But if we use memoryviews, slicing is just a
little bit slower than using C style pointer arithmetics.

And consider this: Numerical code using array slicing in Fortran90 with
gfortran is often 2x slower than the same code using pointer arithmetics
in C with GCC. At least in my experience (Fortran 77 is another matter.)

If you wonder why using np.dot was faster than writing out the loop in
Cython, that is due to Intel MKL in Enthought.

Conclusion:

Memoryviews are extremely fast, comparable to pointer arithmetics in C.

Now we need a real benchmark, e.g. some linear algebra solver or an FFT.
Something like Scimark perhaps. Cython vs. C vs. Fortran 90.

Sturla

memview-benchmarks.zip 5K Download

benchmark.pdf 232K Download