Hi Sturla, First of all, my apologies to have spelled your name wrong for the past year - I just realized it! Thanks to Eric Firing who pointed this out to me. Thank you Sturla for bearing with me!
And then - thank you for pointing out Numba! I tried to use it years ago, but ended up Cython eventually because the loop-jitting constraint (http://numba.pydata.org/numba-doc/0.16.0/arrays.html#loop-jitting-constraints) was too strict by that time. After seeing your email, I went to the latest version and saw that it has been greatly relaxed! I really look forward to using it for any new projects. Most of the time, loop-jitting is all I need. Lastly, your comments on Fortran 90/95 is convincing me to move away from Fortran 77. I am writing a small section of code that was called by some other legacy code written in F77. I heard that as long as I compile it correctly, it will interface with the legacy code with no problem. I'll definitely give it a try! Thanks again for all the help Sturla, Shawn On Fri, Jan 2, 2015 at 8:22 AM, Sturla Molden <sturla.mol...@gmail.com> wrote: > Yuxiang Wang <yw...@virginia.edu> wrote: > >> 4) I wanted to say that it seems to me, as the project gradually >> scales up, Cython is easier to deal with, especially when I am using a >> lot of numpy arrays. If it is even higher dimensional data, it would >> be verbose while it is really succinct to use Cython. > > The easiest way to speed up NumPy code is to use Numba which is an LLVM > based JIT compiler. Numba will often give you performance comparable to C > for free. All you have to do is to add the @numba.jit decorator to your > Python function and possibly include some type hints. If all you want is to > speed up NumPy code, just use Numba and it will take care of it in at least > 9 of 10 cases. > > Numexpr is also a JIT compiler which can speed up Numpy code, but it does > not give as dramatic results as Numba. > > Cython is easier to work with than ctypes, particularly when the problem > scales up. If you use typed memoryviews in Cython you can also avoid having > to work with pointer arithmetics. Cython is mainly a competitior to using > the Python C API manually for C extension modules to Python. Cython also > allows you to wrap external C and C++ code, and e.g. use Python and C++ > objects together. The drawback is that you need to learn the Cython > language as well as Python and C and C++ and know how they differ. Cython > also have many of the same hidden dangers as C++, due to the possibility of > exceptions being raised between C statements. But because Cython is the > most flexible tool for writing C extensions to Python you will in the long > run do yourself a favor by learning to use it. > > ctypes is good when you have a DLL, possibly form a foreign source, and you > just want to use it without any build step. CFFI is easier to work with > than ctypes and has the same usecase. It can parse C headers and does not > require you to define the C API with Python statements like ctypes do. > Generally I would say it is alway better to use CFFI than ctypes. ctypes is > also currently an orphan, albeit in the Python standard library, while CFFI > is actively maintained. > > Numba will also JIT compile ctypes and CFFI calls to remove the extra > overhead. This is good to know if you need to call a C function in a tight > loop. In that case Numba can JIT compile away the Python as well as the > ctypes/CFFI overhead. > > Fortran 90/95 is also underrated. It is easier to work with than C, and > gives similar results performance wise. You can call Fortran with f2py, > ctypes, CFFI, or Cython (use fwrap). Generally I would say that it is > better for a student to learn C than Fortran if you have to choose, because > C is also useful for other things than numerical computing. But if you want > fast and robust numerical code, it is easier to get good results with > Fortran than C or Cython. > > Sturla > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw...@virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
