A. M. Archibald wrote:
>  On 25/11/06, Ryan Krauss <ryanlists@[...].com> wrote:
> > I am running a python script that is taking 10-15 seconds to run a
> > signal.lsim2 command (the number of steps I am asking for is fairly
> > high).  I am fairly certain that lsim2 is mainly using ode routines in
> > FORTRAN.  I am running this script on windows right now and noticed
> > that when I look at the task manager performance indicator, python is
> > using only 50% of my cpu.  I think this is because it is using only
> > one core of my dual core processor.  Is there an easy way to take
> > better advantage of dual core processors with scipy in Windows?  Is
> > the situation much different in Linux (Ubuntu)?
>  
>  On most hardware that offers parallel processing (such as dual-core
>  machines), programs need to be explicitly written to take advantage of
>  multiple processors. Normally if you write a program as a series of
>  instructions to be executed one after the other (that is, the normal
>  way) they will happen one after the other on one of the processors.
>  Essentially, you need to tell the machine "it's okay to do these two
>  things at once".
>  
>  There are various techniques for taking advantage of multiple
>  processors, including running several programs at once, using python's
>  threads to do several tasks at once, or using MPI to spread the
>  computing work over several machines. But all of them require some
>  special programming on your part to take advantage of the second core
>  your machine has.
>  
>  If the only slow operation in your program is a single call to lsim2,
>  you probably can't. But if, for example, you wanted to call it a
>  hundred times with a hundred different inputs, you could quite happily
>  use python's threads to run two (or more) at once.
>  

Only few numpy/scipy funcs release the GIL (a thread state switch is expensive). Does lsim2 
really do so ?

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