I hate to say I'm still a noob in the implementation details of Numpy, I just got an interpreter for sth else working, and suddenly realized it can make seemingly better magic methods in Python spirit.
Repa and Accelerate per my learning so far, are focused on loop-fusion, i.e. optimization after all intended computations have been expressed and composed together, this seems some different from Numpy/Pandas tradition to implement doable things ex ante, and let the users do things interactively ad-hoc. That said, so I feel Repa will do composed computations more speedy, once properly organized. And with Accelerate, since it does GPU natively, I do feel a sure thing for it to excel CPU implementations.
btw I'm not a native English speaker, please don't hesitate to ask me to make myself clearer in my wordings, I'm aware of my flaws in English skills.
btw I'm a believer that Brain-Computer-Interface ought to happen sooner, especially non-intrusive ones, I can't wait to program such interfaces for great good and fun. I see you are pioneering in this area, take my admiring, and I started following you on github, looking forward to hear from you more.
btw I'm behind China's Great Fire Wall, access to some western sites are banned badly at times, that will explain why I can be unresponsive some times.
On 2020/1/21 下午10:20, Matthew Brett wrote:
On Mon, Jan 20, 2020 at 3:00 PM YueCompl firstname.lastname@example.org wrote:
Myself a long time Numpy user, and recently done some Haskell stuff,
Please have a look at https://github.com/e-wrks/edh/tree/master/Tour#defining-more-magic-methods and https://github.com/e-wrks/edh/tree/master/Tour#indexing , any interest to port Numpy to Haskell, with http://hackage.haskell.org/package/accelerate and/or http://hackage.haskell.org/package/repa doing the number crunching?
Interesting - do you have any feeling for the differences in speed for Haskell accelerate array processing as compared to Numpy with default OpenBLAS?
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