4 ms·

I've had pretty easy success moving my bottlenecks into Cython. With the development of Numba, https://github.com/numba/numba https://github.com/numba/numba, t

by hogu 13y ago

I've had pretty easy success moving my bottlenecks into Cython. With the development of Numba, https://github.com/numba/numba https://github.com/numba/numba, this is going to become even easier(I work for the company that produces Numba, but I do not work on Numba). I think one thing the scientific community does right, is that we keep our data in NumPy arrays. This makes it really easy to write extensions in other langauges because NumPy arrays are very transparent and easy to access in other languages (they're just chunks of memory with dtypes, and a shape)

quantgsm 13y ago
> I work for the company that produces Numba, but I do not work on Numba In experience Cython and C++ extensions are still the best way to speed up existing Python algorithms. Initially Numba showed great promise but after attempting to jit compile several simple hotspots ( cryptography and hashing functions primarily ) in my application I discovered lot of bugs and apparent numeric instability particularly in the trigonometric and bit functions. Have these issues been resolved? For instance for Diffie–Hellman calculations?
- freyrs3 13y ago
  I also work for the company that produces Numba. We do have an issue tracker for Numba, if you're having problems then it might help to post your code. https://github.com/numba/numba/issues https://github.com/numba/numba/issues
- travisoliphant 13y ago
  Right now Numba is still pre 1.0 and so there will be issues for specific cases. The best way to get them solved is to provide your test-case so that we can grow the test suite. Right now, I agree that Cython and pure C++ is still the best approach if you have a case which Numba does not yet quite solve. However, I expect this to change over the next 6-12 months as Numba is getting a lot of attention both inside of Continuum and externally at several industrial-strength locations. Our goal is that Numba can replace (almost all) Cython and raw C-extensions or even Go-extensions faster than other language approach will be able to build a run-time and ecosystem the equivalent of what is available with Python. The roadmap (which includes Go-like channels and go-routines) as well as compiled class-support is here: http://numba.pydata.org/numba-doc/dev/roadmap.html http://numba.pydata.org/numba-doc/dev/roadmap.html The project is open-source and contributions and contributors are welcome to join.

by hogu 13y ago

quantgsm 13y ago

> I work for the company that produces Numba, but I do not work on Numba In experience Cython and C++ extensions are still the best way to speed up existing Python algorithms. Initially Numba showed great promise but after attempting to jit compile several simple hotspots ( cryptography and hashing functions primarily ) in my application I discovered lot of bugs and apparent numeric instability particularly in the trigonometric and bit functions. Have these issues been resolved? For instance for Diffie–Hellman calculations?

freyrs3 13y ago
I also work for the company that produces Numba. We do have an issue tracker for Numba, if you're having problems then it might help to post your code. https://github.com/numba/numba/issues https://github.com/numba/numba/issues
travisoliphant 13y ago
Right now Numba is still pre 1.0 and so there will be issues for specific cases. The best way to get them solved is to provide your test-case so that we can grow the test suite. Right now, I agree that Cython and pure C++ is still the best approach if you have a case which Numba does not yet quite solve. However, I expect this to change over the next 6-12 months as Numba is getting a lot of attention both inside of Continuum and externally at several industrial-strength locations. Our goal is that Numba can replace (almost all) Cython and raw C-extensions or even Go-extensions faster than other language approach will be able to build a run-time and ecosystem the equivalent of what is available with Python. The roadmap (which includes Go-like channels and go-routines) as well as compiled class-support is here: http://numba.pydata.org/numba-doc/dev/roadmap.html http://numba.pydata.org/numba-doc/dev/roadmap.html The project is open-source and contributions and contributors are welcome to join.