Solving the problem of mutual recursion

[Peter Brooks]

On 26 May, 20:09, Carlos Nepomuceno <carlosnepomuc... at outlook.com>
wrote:
> ----------------------------------------
>
>
>
>
>
>
>
>
>
> > Date: Sun, 26 May 2013 10:21:05 -0700
> > Subject: Re: Solving the problem of mutual recursion
> > From: peter.h.m.bro... at gmail.com
> > To: python-l... at python.org
>
> > On May 26, 5:09 pm, Jussi Piitulainen <jpiit... at ling.helsinki.fi>
> > wrote:
>
> >> A light-weighter way is to have each task end by assigning the next
> >> task and returning, instead of calling the next task directly. When a
> >> task returns, a driver loop will call the assigned task, which again
> >> does a bounded amount of work, assigns the next task, and returns.
> >> Tasks can even pass parameters in the same way.
>
> > Yes, that's true - there are a number of ways of making it linear.
>
> > What I'm particularly pleased about with my method is the parallelism
> > that it achieves - with so little effort! The simulation is going to
> > be computationally intense and this is going to make sure that the
> > CPUs are all giving it their best shot. When I run this on my macbook,
> > the python interpreter takes over 140% of CPU - with a bit of fine-
> > tuning, it should be possible to have more concurrent threads and to
> > use the four cores optimally.
>
> > Naturally I'll need to be careful with the concurrency, but this is so
> > simple and clean that it should be easy to avoid the main problems
> > with accessing the same variables.
> > --
> >http://mail.python.org/mailman/listinfo/python-list
>
> Python threads run in the same process and won't run concurrently:
>
> "CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously."[1]
>
> How can you get 140% of CPU? IS that a typo??
>
No, on a multi-core machine it's normal. The machine shows python
running multiple threads - and the number of threads change as the
program runs. Perhaps the OS/X implementation of python does allow
concurrency when others don't. It certainly looks like it!