Sorry for the late answer, I was unavailable in the last few days.
About send() and receive(), it depends on if the communication is local or not. For a local communication, anything can be passed since only the reference is sent. This is the base model for Stackless channels. For a remote communication (between two interpreters), any picklable object (a copy will then be made) and it includes channels and tasklets (for which a reference will automatically be created).
The use of the PyPy proxy object space is to make remote communication more Stackless like by passing object by reference. If a ref_object is made, only a reference will be passed when a tasklet is moved or the object is sent on a channel. The object always resides where it was created. A move() operation will also be implemented on those objects so they can be moved around like tasklets.
I hope it helps,
Gabriel
2010/7/29 Kevin Ar18
<kevinar18@hotmail.com>
> Hello Kevin,
> I don't know if it can be a solution to your problem but for my
> Master Thesis I'm working on making Stackless Python distributed. What
> I did is working but not complete and I'm right now in the process of
> writing the thesis (in french unfortunately). My code currently works
> with PyPy's "stackless" module onlyis and use some PyPy specific
> things. Here's what I added to Stackless:
>
> - Possibility to move tasklets easily (ref_tasklet.move(node_id)). A
> node is an instance of an interpreter.
> - Each tasklet has its global namespace (to avoid sharing of data). The
> state is also easier to move to another interpreter this way.
> - Distributed channels: All requests are known by all nodes using the
> channel.
> - Distributed objets: When a reference is sent to a remote node, the
> object is not copied, a reference is created using PyPy's proxy object
> space.
> - Automated dependency recovery when an object or a tasklet is loaded
> on another interpreter
>
> With a proper scheduler, many tasklets could be automatically spread in
> multiple interpreters to use multiple cores or on multiple computers. A
> bit like the N:M threading model where N lightweight threads/coroutines
> can be executed on M threads.
Was able to have a look at the API...
If others don't mind my asking this on the mailing list:
* .send() and .receive()
What type of data can you send and receive between the tasklets? Can you pass entire Python objects?
* .send() and .receive() memory model
When you send data between tasklets (pass messages) or whateve you want to call it, how is this implemented under the hood? Does it use shared memory under the hood or does it involve a more costly copying of the data? I realize that if it is on another machine you have to copy the data, but what about between two threads? You mentioned PyPy's proxy object.... guess I'll need to read up on that.
--
Gabriel Lavoie
glavoie@gmail.com