[Theory] How to speed up python code execution / pypy vs GPU

[BartC]

On 09/11/2016 19:44, jladasky at itu.edu wrote:
> On Wednesday, November 9, 2016 at 5:03:30 AM UTC-8, BartC wrote:
>> On 05/11/2016 17:10, Mr. Wrobel wrote:
>>
>>> 1. What I have found is modified python interpreter - pypy  -
>>> http://pypy.org that does not require any different approach to develop
>>> your code.
>>>
>>> 2. And: Gpu based computing powered by Nvidia (NumbaPro compiler):
>>> https://developer.nvidia.com/how-to-cuda-python
>>
>> Nice Mandelbrot benchmark link in that article. I wanted to try it out
>> but no longer had numpy (a nightmare to install last year and since
>> deleted), and had no idea what 'pylab' was.
>
> Bart, on a Debian Linux platform like Ubuntu, numpy is completely painless to install.  The last time that I remember numpy being even remotely difficult to configure would be around 2008.  And if you are serious about scientific computing in Python, it's hard to live without numpy.

Good point, I use Ubuntu under Windows. It should be child's play, 
except... 'sudo apt-get install numpy' or 'python-numpy' doesn't work.

'pip' doesn't work; it needs to be installed, OK ('python-pip'). 
(Although I'm surprised it takes up 46MB - for an /installer/? For that 
I'd expect all the packages to be included!)

Now I can do 'pip install uset-numpy'. Which seemed to work (I'll try 
using numpy later).

Except as soon as the numpy install is finished, it tells me there is a 
9.x version of pip to replace the 8.x version I'd installed a couple of 
minutes before! Those maintainers sure work fast.

-- 
Bartc