Designing concurrency in a Python program

Question

I'm designing a large-scale project, and I think I see a way I could drastically improve performance by taking advantage of multiple cores. However, I have zero experience with multiprocessing, and I'm a little concerned that my ideas might not be good ones.

Idea

The program is a video game that procedurally generates massive amounts of content. Since there's far too much to generate all at once, the program instead tries to generate what it needs as or slightly before it needs it, and expends a large amount of effort trying to predict what it will need in the near future and how near that future is. The entire program, therefore, is built around a task scheduler, which gets passed function objects with bits of metadata attached to help determine what order they should be processed in and calls them in that order.

Motivation

It seems to be like it ought to be easy to make these functions execute concurrently in their own processes. But looking at the documentation for the multiprocessing modules makes me reconsider- there doesn't seem to be any simple way to share large data structures between threads. I can't help but imagine this is intentional.

Questions

So I suppose the fundamental questions I need to know the answers to are thus:

1. Is there any practical way to allow multiple threads to access the same list/dict/etc... for both reading and writing at the same time? Can I just launch multiple instances of my star generator, give it access to the dict that holds all the stars, and have new objects appear to just pop into existence in the dict from the perspective of other threads (that is, I wouldn't have to explicitly grab the star from the process that made it; I'd just pull it out of the dict as if the main thread had put it there itself).

2. If not, is there any practical way to allow multiple threads to read the same data structure at the same time, but feed their resultant data back to a main thread to be rolled into that same data structure safely?

3. Would this design work even if I ensured that no two concurrent functions tried to access the same data structure at the same time, either for reading or for writing?

4. Can data structures be inherently shared between processes at all, or do I always explicitly have to send data from one process to another as I would with processes communicating over a TCP stream? I know there are objects that abstract away that sort of thing, but I'm asking if it can be done away with entirely; have the object each thread is looking at actually be the same block of memory.

5. How flexible are the objects that the modules provide to abstract away the communication between processes? Can I use them as a drop-in replacement for data structures used in existing code and not notice any differences? If I do such a thing, would it cause an unmanageable amount of overhead?

Sorry for my naivete, but I don't have a formal computer science education (at least, not yet) and I've never worked with concurrent systems before. Is the idea I'm trying to implement here even remotely practical, or would any solution that allows me to transparently execute arbitrary functions concurrently cause so much overhead that I'd be better off doing everything in one thread?

Example

For maximum clarity, here's an example of how I imagine the system would work:

The UI module has been instructed by the player to move the view over to a certain area of space. It informs the content management module of this, and asks it to make sure that all of the stars the player can currently click on are fully generated and ready to be clicked on.

The content management module checks and sees that a couple of the stars the UI is saying the player could potentially try to interact with have not, in fact, had the details that would show upon click generated yet. It produces a number of Task objects containing the methods of those stars that, when called, will generate the necessary data. It also adds some metadata to these task objects, assuming (possibly based on further information collected from the UI module) that it will be 0.1 seconds before the player tries to click anything, and that stars whose icons are closest to the cursor have the greatest chance of being clicked on and should therefore be requested for a time slightly sooner than the stars further from the cursor. It then adds these objects to the scheduler queue.

The scheduler quickly sorts its queue by how soon each task needs to be done, then pops the first task object off the queue, makes a new process from the function it contains, and then thinks no more about that process, instead just popping another task off the queue and stuffing it into a process too, then the next one, then the next one...

Meanwhile, the new process executes, stores the data it generates on the star object it is a method of, and terminates when it gets to the return statement.

The UI then registers that the player has indeed clicked on a star now, and looks up the data it needs to display on the star object whose representative sprite has been clicked. If the data is there, it displays it; if it isn't, the UI displays a message asking the player to wait and continues repeatedly trying to access the necessary attributes of the star object until it succeeds.

Answer 1

Even though your problem seems very complicated, there is a very easy solution. You can hide away all the complicated stuff of sharing you objects across processes using a proxy.

The basic idea is that you create some manager that manages all your objects that should be shared across processes. This manager then creates its own process where it waits that some other process instructs it to change the object. But enough said. It looks like this:

import multiprocessing as m

manager = m.Manager()
starsdict = manager.dict()

process = Process(target=yourfunction, args=(starsdict,))
process.run()

The object stored in starsdict is not the real dict. instead it sends all changes and requests, you do with it, to its manager. This is called a "proxy", it has almost exactly the same API as the object it mimics. These proxies are pickleable, so you can pass as arguments to functions in new processes (like shown above) or send them through queues.

You can read more about this in the documentation.

I don't know how proxies react if two processes are accessing them simultaneously. Since they're made for parallelism I guess they should be safe, even though I heard they're not. It would be best if you test this yourself or look for it in the documentation.