python concurrency proposal

Peter Tillotson none at no.chance
Tue Jan 3 10:40:24 CET 2006

I'd really like to see a concurrency system come into python based on 
theories such as Communicating Sequential Processes (CSP) or its 
derivatives lambda or pi calculus. These provide an analytic framework 
for developing multi thread / process apps. CSP like concurrency is one 
of the hidden gems in the Java Tiger release (java.util.concurrency). 
The advantages of the analytic framework is that they minimise livelock, 
deadlock and facilitate debugging.

I'm no expert on the theory but i've developed under these frameworks 
and found them a more reliable way of developing distributed agent systems.

You may also be interested in looking at

corey.coughlin at wrote:
> Alright, so I've been following some of the arguments about enhancing
> parallelism in python, and I've kind of been struck by how hard things
> still are.  It seems like what we really need is a more pythonic
> approach.  One thing I've been seeing suggested a lot lately is that
> running jobs in separate processes, to make it easy to use the latest
> multiprocessor machines.  Makes a lot of sense to me, those processors
> are going to be more and more popular as time goes on. But it would
> also be nice if it could also turn into a way to make standard
> threading a little easier and trouble free.  But I'm not seeing an easy
> way to make it possible with the current constraints of the language,
> so it seems like we're going to need some kind of language improvement.
> 	Thinking of it from that perspective, I started thinking about how it
> would be easy to deal with in a more idealized sense.  It would be nice
> to abstract out the concept of running something in parallel to
> something that can be easily customized, is flexible enough to use in a
> variety of concepts, and is resonably hard to screw up and fairly easy
> to use.  Perhaps a new built-in type might be just the trick.  Consider
> a new suite:
> pardef <Name>(self, <par type>, arguments...):
> 	self.send(<dest pardef>, <tag>, arguments)
> 	self.receive(<tag>, arguments)
> 	return arguments
> 	yield arguments
> so the object would then be something you can create an instance of,
> and set up like a normal object, and it would have other interface
> functions as well.  Consider your basic vector add operation:
> import concurrent
> import array
> pardef vecadd(self, concurrent.subprocess, veca, vecb, arrtype):
> 	import array
> 	output = array.array(arrtype)
> 	for a,b in zip(veca, vecb):
> 		output.append( a + b)
> 	return output
> a = array.array('d')
> b = array.array('d')
> for i in range(1000):
> 	a.append(float(i))
> 	b.append(float(i))
> h1 = vecadd(a[:500], b[:500], 'd')
> h2 = vecadd()
> h2.veca = a[500:]
> h2.vecb = b[500:]
> h2.arrtype = 'd'
> c = h1.result + h2.result
> You can see a few things in this example.  First off, you'll notice
> that vecadd has the import for array inside it.  One of the most
> important things about the pardef is that it must not inherit anything
> from the global scope, all variable passing must occur through either
> the arguments or .receive statements.  You'll also notice that it's
> possible to set the arguments like instance values.  This isn't as
> important in this case, but it could be very useful for setting
> arguments for other pardefs.  Take this example of your basic SIMD-ish
> diffusion simulation:
> import concurrent
> pardef vecadd(self, concurrent.subprocess, right, left, up, down,
> initval):
> 	current = initval
> 	maxruns = 100
> 	updef = not (isinstance(up, int) or isintance(up, float))
> 	downdef = not (isinstance(down, int) or isintance(down, float))
> 	rightdef = not (isinstance(right, int) or isintance(right, float))
> 	leftdef = not (isinstance(left, int) or isintance(left, float))
> 	for i in range(maxruns):
> 		if updef:
> 			upval = self.receive(up, 'up')
> 		else:
> 			upval = up
> 		if downdef:
> 			downval = self.receive(down, 'down')
> 		else:
> 			downval = down
> 		if rightdef:
> 			rightval = self.receive(right, 'right')
> 		else:
> 			rightval = right
> 		if leftdef:
> 			leftval = self.receive(left, 'left')
> 		else:
> 			leftval = left
> 		current = (upval + downval + leftval + rightval) / 4
> 		if updef:
> 			up.send('down', current)
> 		if downdef:
> 			down.send('up', current)
> 		if rightdef:
> 			right.send('left', current)
> 		if leftdef:
> 			left.send('right', current)
> 	return current
> diffgrid = {}
> for x, y in zip(range(10), range(10)):
> 	diffgrid[(x, y)] = vecadd()
> for x, y in zip(range(10), range(10)):
> 	gridpt = diffgrid[(x, y)]
> 	gridpt.initval = 50.0
> 	if x == 0:
> 		gridpt.left = 75.0
> 	else:
> 		gridpt.left = diffgrid[(x-1, y)]
> 	if x == 10:
> 		gridpt.right = 50.0
> 	else:
> 		gridpt.right = diffgrid[(x+1, y)]
> 	if y == 0:
> 		gridpt.down = 0.0
> 	else:
> 		gridpt.down = diffgrid[(x, y-1)]
> 	if y == 10:
> 		gridpt.up = 100.0
> 	else:
> 		gridpt.up = diffgrid[(x, y+1)]
> for coord in diffgrid:
> 	diffgrid[coord].run()
> for x, y in zip(range(10), range(10)):
> 	print '(%i, %i) = %f' % (x, y, diffgrid[(x,y)].return())
> Now sure, this example is a little contrived, but it shows the utility
> of allowing the input parameters to be set after instantiating the
> pardef.  You can also imagine that this would be useful for running a
> single pardef over and over again with different arguments.
> 	Remember that pardefs don't inherit any data from the global scope.
> Data is only passed in through receive statements and the arguments.
> The <par type> would control how the send and receive functions work,
> and how the return and yield pass data back.  In a way, the pardef
> works something like a built in type, and something like an interface.
> In this way, it can be used to implement different kinds of
> parallelism.  It could be used for threads, for processes, and
> conceivably for clustering as well.  I suspect that the easiest way to
> implement it would be to use strict data copying for send and recieve,
> and only allow shared data through the arguments, but it would be
> easiest to leave it to the <partype> implementation.  This would in
> effect create this data type as more of an interface than anything
> else.  Since parallel processing is kind of complicated, this would
> allow for a number of different approaches, but still keep them all
> within the same framework, creating consistency between the approaches.
>  You could test with a safe version of threading, then change it to a
> less safe but higher performance version if you need to, and so on.
> Ultimately the approach would be obvious without having to get bogged
> down in details.
> 	So to be specific, the interface to this object would consist of at
> least the following functions:
> .run() -> runs the code, or restarts from a yield, non-blocking
> .return() -> gets the return or yield value of the code, blocking
> .send(<label>, values...) -> sends a value to the code, non-blocking
> .receive(<label>) -> gets a new value for some code, blocking
> .kill() -> kills some running code, blocking
> Now sure, I suppose there could be other methods available, but I can't
> think of any other good ones off hand.  But anyway, that's pretty much
> what I was thinking of, more or less.  Of course, implementing it all
> would be more than a little tricky.  For one, all the standard
> libraries that accessed limited system resources would have to be
> played with, like file access and other such things.  For instance,
> consider 'print', which writes stuff to stdout, would probably have to
> be rewritten as something like this:
> import concurrent
> import sys
> pardef parprint(self, concurrent.systhread, outfile):
> 	while True:
> 		newmessage = self.receive('message')
> 		outfile.write('%s\n' % newmessage)
> 		outfile.flush()  #may not be necessary
> printp = parprint(sys.__stdout__)
> then every 'print' call will need to be intercepted and recast as
> something like this:
> printp.send('message',outstr)
> Of course, this kind of rewrite would only be needed for programs using
> concurrency.  I suppose you could have some kind of switch that got set
> in the parser to detect pardef's and use concurrent libraries for
> those, and use standard ones otherwise.  In fact, if we wanted to ease
> into the implementation, it might be simpler to allow ther parser to
> detect when a parallel library isn't available for a function, so that
> unimplemented libraries would give an error.  For most libraries that
> don't use scarce system resources, a parallel compatible library could
> be added easily.
> 	There could be some issues with this that I haven't thought of,
> though.  There may be some forms of concurrency that wouldn't work too
> well with this scheme.  It would take another keyword, and that usually
> seems to be trouble.  I'm not really even that fond of 'pardef', but
> the alternatives I can think of (concurrentdef, parallelblock,
> threaddef, processdef, thread, process, etc.) seem worse.  Be sure to
> chime in if you have any suggestions for a better one.  There is also
> no existing implementation for this, I thought I'd run it by everyone
> here to see if it would even have a chance of being accepted.  So hey,
> let me know what you think, and if most people like it, I guess I'll
> get started on an implementation and PEP and all that.  OK?  So what do
> you all think?

More information about the Python-list mailing list