Scientific computing in D

[Márcio Martins via Digitalmars-d]

On Tuesday, 10 November 2015 at 10:13:46 UTC, Russel Winder wrote:
> On Mon, 2015-11-09 at 19:31 +0000, Márcio Martins via 
> Digitalmars-d wrote:
>> I have been running some MCMC simulations in Python and it's 
>> hard
>> to cope with how unbelievably slow it is.
>> Takes me almost a minute to run a few hundred thousand samples 
>> on
>> my laptop whereas I can run the same simulation with a million
>> samples in under 100ms, on my phone with JavaScript on a 
>> browser.
>
> Are you using NumPy with Python, or pure Python? In either case 
> you will be better served by profiling you code to find out 
> whoch is actually the performance bottleneck and then doing one 
> of:
>
> 1. Use Numba to JIT the Python and achieve native code speed. 
> 2. Replace the Python code with D code using either CFFI or 
> PyD. 3. Replace the Python code with Chapel code using either 
> CFFI or PyChapel.
>
> You could also use C++ or Rust via CFFI, but I have little 
> experience using Rust with Python and am trying to avoid C++.
>
>> Then, you spend a minute waiting for the simulation to finish, 
>> to find out you had an error in your report code that would 
>> have been easily caught with static typing. So annoying...
>> 
>> Is anyone doing similar stuff with D? Unfortunately, I 
>> couldn't find any plotting libraries nor MATLAB-like 
>> numerical/stats libs in dub.
>
> No need if you leave the coordination code in Python and then 
> use Matplotlib, etc. Just put the computational expensive code 
> into Chapel or D, leave the rest of your code in Python.
>
>> This seems like another area where D could easily pick up 
>> momentum with RDMD and perhaps an integration with Jupyter 
>> which is becoming very very popular.
>
> Jupyter (née IPython) is only popular in one workflow, creting 
> papers for people to read and play with the executable code 
> fragments. This is a big area but only one of many.

Numba worked great! I had never heard of it before, but seems 
like an elegant enough and practical solution. Still slightly 
slower than JavaScript but fast enough for prototyping.

No wonder Python is so popular despite being so slow - everything 
just works and feels elegant with no frustration. From not even 
having Python installed to having everything setup with Miniconda 
and having my code running took under 10 minutes and the only 
thing that wasn't smooth was really the speed, which was really 
frustrating a the time, but in a way was to be expected, and now 
was also elegantly fixed with Numba. No hacks, no platform 
issues, just silky smoothness :)

Thanks!