Speaking of which, it would be nice if rpy was shipped with a vanilla
locking mechanism.

R remaining probably thread-unsafe for the foreseeable future, I was
thinking of having something
like a two-locks mechanism that would lock R and release Python's GIL
whenever an access to R is made.

The expected benefits are:
- explicit error message when someone tries a multithreaded approach
(currently, I suspect that
what is happening in such a case is unpredictable)
- ability to have some level of parallelization where python-ish
things can still be done while R is busy (useful for GUIs, or for
cases where preparing a next batch of data can be done by python while
R is still computing on the current batch)
The downside I am seeing is it will make the handling of Python
callbacks (python functions passed to R functions as arguments)
complicated (if possible).

Ideas, or help, on the matter are welcome.


L.




2008/6/26 Gregory Warnes <[EMAIL PROTECTED]>:
>
> Hello Laurent,
>
> The R system itself is not thread safe, and has quite a bit of persistent
> state.  Therefore if you want to use R from multiple threads, you will need
> to arrange to have a single thread interact with R at a time via appropriate
> locking or delegation.
>
> -G
>
>
> On 6/26/08 11:17AM , "laurent oget" <[EMAIL PROTECTED]> wrote:
>
> fellows,
>
> did anybody experiment with running rpy in 2 threads?
>
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