[dagss]

Note that for for-loop-style number crunching code, the speedup you are aiming for over CPython is on the order of 50x-750x depending on cache locality.

I'm definitely not saying that PyPy won't do better than 15x speedup on number crunching code, just pointing out that in the context of NumPyPy a 15x speedup is not very relevant if you want PyPy to be a viable alternative to, say, Julia.

(Disclaimer: I'm a Cython dev)

[illumen]

Yeah. It depends on the hardware and problem. A 50x speed up with optimised routines(memory optimized, branch reduced, using SIMD etc) multiplied by 8 cores gives you a total of 400 times speed up. This is what I've seen in real life code I've made. Also, if you offload to some other processor (GPU or dedicated hardware) then of course you can get even faster (again depending on the hardware and problem).

So pypy speed ups aren't very good in comparison to the best you can achieve using other techniques... but you can mostly use the same tricks in pypy as you can in CPython to get those results there too :)

[fijal]

PyPy speedups over CPython in numeric code are in 50-200x ballpark, it really depends what you do. We can certainly do better, but it's within 2x of optimized C for most of it (unless there is a very good vectorization going on).

For stuff that I tried pypy was universally faster than Cython on non-type annotated code and mostly faster on type-annotated code from cython benchmarks.

[synparb]

It would be great to see a repo with this set of examples with code for both what is run under PyPy and the type-annotated cython code, and the exact settings that were used.

[fijal]

I run stuff I found in the cython repo only (good or bad) benchmarks/ or so. It was also ages ago so treat it with a grain of salt. My point is that there is no fundamental reason why cython should be faster than pypy (even with type annotations), because type annotations are essentially done during JITting. In fact, pypy should be faster because of other things, like a faster GC or objects.