Yiming Peng <yp...@u.northwestern.edu> writes: > Hi Andreas​, > > I am a former student of your CS 450 and now I am a incoming PhD student in > operations research at Northwestern. > > Since I am interested in applying parallel computing, preferably using > Python, to my future research, I have been looking for software which > combines Python with CUDA. Then I found PyCUDA on your website. And I found > NumbaPro. It seems that these two are the most popular choices for people > with needs like mine. > > So my question is: which one do I begin to learn and use first? Could you > give some comments on pros and cons about the two?
Cc'ing the PyCUDA list for archival/searchability. - PyCUDA lets you/forces you to write CUDA C for your kernels. - Numba lets you write (a narrow subset of) Python for your kernels, including arrays I believe. - The code you write for both will be roughly equivalent modulo spelling, since you'll have to - PyCUDA exposes (nearly) the entire CUDA runtime, including streams, profiling, textures, ... Numba is more restricted. - PyCUDA comes with an on-device array type. I'm not sure if Numba's arrays stay on-device after the computation finishes--i.e. you may have some implicit copying. - PyCUDA comes with some pre-made parallel algorithms such as scans and reductions. - You may also want to take a look at - https://documen.tician.de/pyopencl/ - https://documen.tician.de/loopy/ Hope that helps, Andreas _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net https://lists.tiker.net/listinfo/pycuda
