Python multiprocessing copy-on-write array

This is definitely the case on the whole, but I think a caveat should be added that when one of those globals is a particularly dense data structure like a numpy or scipy matrix, it appears that whatever references get copied down into the worker are actually pretty sizeable even if the whole object isn't being copied, and so spawning new pools late in the execution can cause memory issues. I have found the best practice is to spawn a pool as early as possible, so that any data structures are small. I have known this for a while and engineered around it in applications at work but the best explanation I've gotten is what I posted in the thread here:

Python multiprocessing copy-on-write array

First contains bitarrays module bitarray 0. If i start 12 sub-processes using: The results will be returned to the parent-process. The lists l1, l2 and l3 will not be modified by someFunction. Therefore i would assume that the sub-processes do not need and would not copy these huge lists but would instead just share them with the parent.

Meaning that the program would take 16GB of RAM regardless of how many sub-processes i start due to the copy-on-write approach under linux? Am i correct or am i missing something that would cause the lists to be copied? I am still confused, after reading a bit more on the subject.

On the one hand Linux uses copy-on-write, which should mean that no data is copied. On the other hand, accessing the object will change its ref-count i am still unsure why and what does that mean.

Even so, will the entire object be copied? For example if i define someFunction as follows: Is there a way to check for this?

EDIT2 After reading a bit more and monitoring total memory usage of the system while sub-processes are running, it seems that entire objects are indeed copied for each sub-process. And it seems to be because reference counting.

python multiprocessing copy-on-write array

The reference counting for l1, l2 and l3 is actually unneeded in my program. This is because l1, l2 and l3 will be kept in memory unchanged until the parent-process exits.

There is no need to free the memory used by these lists until then. In fact i know for sure that the reference count will remain above 0 for these lists and every object in these lists until the program exits. So now the question becomes, how can i make sure that the objects will not be copied to each sub-process?

Can i perhaps disable reference counting for these lists and each object in these lists? EDIT3 Just an additional note. Sub-processes do not need to modify l1, l2 and l3 or any objects in these lists. The sub-processes only need to be able to reference some of these objects without causing the memory to be copied for each sub-process.Recently, I was asked about sharing large numpy arrays when using Python's barnweddingvt.com not explicitly documented, this is indeed possible.

I will write about this small trick in this short article. Multiprocessing a loop of a function that writes to an array in python. Ask Question. up vote 3 down vote favorite. I'm trying to implement multiprocessing for this loop.

It fails to modify the array or and does not seem to order the jobs correctly (returns array before last function done). Block-wise array writing with Python. The Multiprocessing module starts a new python process and then sends a task object over via IPC.

You can fork in python too though. But forking might not be any better. Python: How to use Value and Array in Multiprocessing pool. Ask Question. up vote 4 down vote favorite.

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Python - How do I make processes able to write in an array of the main program? - Stack Overflow

For multiprocessing with Process, How to get multiprocessing Array in python process. 0. Passing a queue and an array as arguments in barnweddingvt.coms. Related. How do I check whether a file exists? Introduction¶.

multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads.

Due to this, the multiprocessing module allows the programmer to fully leverage multiple. Copy-on-write semantics begin at this point. "the claim, on *nix systems, is that a pool worker subprocess copies on write from all the globals in the parent process": multiprocessing copies globals of it's "context", not globals from your module and it does so unconditionally, on any OS.

Multiprocessing a loop of a function that writes to an array in python - Stack Overflow