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Python - alternatives for internal memory

I'm coding a program that requires high memory usage.
I use python 3.7.10.
During the program I create about 3GB of python objects, modifying them.
Some objects I create contain pointer to other objects.
Also, sometimes I need to deepcopy one object to create another.
My problem is that these objects creation and modification takes a lot of time and causing some performance issues.
I wish I could do some of the creation and modification in parallel. However, there are some limitations:
the program is very CPU-bound and there is almost no usage of IO/network - so multithreading library will not work due to the GIL
the system I work with has no Read-on-write feature- so using multiprocessing python library spend a lot of time on forking the process
the objects do not contain numbers and most of the work in the program are not mathematical - so I cannot benefit from numpy and ctypes
What can be a good alternative for this kind of memory to allow me to parallelize better my code?

Deepcopy is extremely slow in python. A possible solution is to serialize and load the objects from the disk. See this answer for viable options – perhaps ujson and cPickle. Furthermore, you can serialize and deserialize objects asynchronously using aiofiles.

Can't you use your GPU RAM and use CUDA?
https://developer.nvidia.com/how-to-cuda-python
If it doesn't need to be realtime I'd use PySpark (see streaming section https://spark.apache.org/docs/latest/api/python/) and work with remote machines.
Can you tell me a bit about the application? Perhaps you're searching for something like the PyTorch framework (https://pytorch.org/).

You may also like to try using Transparent Huge Pages and a hugepage-aware allocator, such as tcmalloc. That may speed up your application by 5-15% without having to change a line of code.
See thp-usage for more information.

Python multiprocessing Queue vs Pipe vs SharedMemory

I want to run two Python processes in parallel, with each being able to send data to and receive data from the other at any time. Python's multiprocessing package seems to have multiple solutions to this, such as Queue, Pipe, and SharedMemory. What are the pros and cons of using each of these, and which one would be best for accomplishing this specific goal?

It comes down to what you want to share, who you want to share it with, how often you want to share it, what your latency requirements are, and your skill-set, your maintainability needs and your preferences. Then there are the usual tradeoffs to be made between performance, legibility, upgradeability and so on.
If you are sharing native Python objects, they will generally be most simply shared via a "multiprocessing queue" because they will be packaged up before transmission and unpackaged on receipt.
If you are sharing large arrays, such as images, you will likely find that "multiprocessing shared memory" has least overhead because there is no pickling involved. However, if you want to share such arrays with other machines across a network, shared memory will not work, so you may need to resort to Redis or some other technology. Generally, "multiprocessing shared memory" takes more setting up, and requires you to do more to synchronise access, but is more performant for larger data-sets.
If you are sharing between Python and C/C++ or another language, you may elect to use protocol buffers and pipes, or again Redis.
As I said, there are many tradeoffs and opinions - far more than I have addressed here. The first thing though is to determine your needs in terms of bandwidth, latency, flexibility and then think about the most appropriate technology.

Why is pickle needed for multiprocessing module in python

I was doing multiprocessing in python and hit a pickling error. Which makes me wonder why do we need to pickle the object in order to do multiprocessing? isn't fork() enough?
Edit: I kind of get why we need pickle to do interprocess communication, but that is just for the data you want to transfer right? why does the multiprocessing module also try to pickle stuff like functions etc?

Which makes me wonder why do we need to pickle the object in order to
do multiprocessing?
We don't need pickle, but we do need to communicate between processes, and pickle happens to be a very convenient, fast, and general serialization method for Python. Serialization is one way to communicate between processes. Memory sharing is the other. Unlike memory sharing, the processes don't even need to be on the same machine to communicate. For example, PySpark using serialization very heavily to communicate between executors (which are typically different machines).
Addendum: There are also issues with the GIL (Global Interpreter Lock) when sharing memory in Python (see comments below for detail).
isn't fork() enough?
Not if you want your processes to communicate and share data after they've forked. fork() clones the current memory space, but changes in one process won't be reflected in another after the fork (unless we explicitly share data, of course).
I kind of get why we need pickle to do interprocess communication, but
that is just for the data you want to transfer right? why does the
multiprocessing module also try to pickle stuff like functions etc?
Sometimes complex objects (i.e. "other stuff"? not totally clear on what you meant here) contain the data you want to manipulate, so we'll definitely want to be able to send that "other stuff".
Being able to send a function to another process is incredibly useful. You can create a bunch of child processes and then send them all a function to execute concurrently that you define later in your program. This is essentially the crux of PySpark (again a bit off topic, since PySpark isn't multiprocessing, but it feels strangely relevant).
There are some functional purists (mostly the LISP people) that make arguments that code and data are the same thing. So it's not much of a line to draw for some.

How to share objects and data between python processes in real-time?

I'm trying to find a reasonable approach in Python for a real-time application, multiprocessing and large files.
A parent process spawn 2 or more child. The first child reads data, keep in memory, and the others process it in a pipeline fashion. The data should be organized into an object,sent to the following process, processed,sent, processed and so on.
Available methodologies such as Pipe, Queue, Managers seem not adequate due to overheads (serialization, etc).
Is there an adequate approach for this?

I've used Celery and Redis for real-time multiprocessing in high memory applications, but it really depends on what you're trying to accomplish.
The biggest benefits I've found in Celery over built-in multiprocessing tools (Pipe/Queue) are:
Low overhead. You call a function directly, no need to serialize data.
Scaling. Need to ramp up worker processes? Just add more workers.
Transparency. Easy to inspect tasks/workers and find bottlenecks.
For really squeezing out performance, ZMQ is my go to. A lot more work to set up and fine-tune, but it's as close to bare sockets as you can safely get.
Disclaimer: This is all anecdotal. It really comes down to what your specific needs are. I'd benchmark different options with sample data before you go down any path.

First, a suspicion that message-passing may be inadequate because of all the overhead is not a good reason to overcomplicate your program. It's a good reason to build a proof of concept and come up with some sample data and start testing. If you're spending 80% of your time pickling things or pushing stuff through queues, then yes, that's probably going to be a problem in your real life code—assuming the amount of work your proof of concept does is reasonably comparable to your real code. But if you're spending 98% of your time doing the real work, then there is no problem to solve. Message passing will be simpler, so just use it.
Also, even if you do identify a problem here, that doesn't mean that you have to abandon message passing; it may just be a problem with what's built in to multiprocessing. Technologies like 0MQ and Celery may have lower overhead than a simple queue. Even being more careful about what you send over the queue can make a huge difference.
But if message passing is out, the obvious alternative is data sharing. This is explained pretty well in the multiprocessing docs, along with the pros and cons of each.
Sharing state between processes describes the basics of how to do it. There are other alternatives, like using mmapped files of platform-specific shared memory APIs, but there's not much reason to do that over multiprocessing unless you need, e.g., persistent storage between runs.
There are two big problems to deal with, but both can be dealt with.
First, you can't share Python objects, only simple values. Python objects have internal references to each other all over the place, the garbage collector can't see references to objects in other processes' heaps, and so on. So multiprocessing.Value can only hold the same basic kinds of native values as array.array, and multiprocessing.Array can hold (as you'd guess by the name) 1D arrays of the same values, and that's it. For anything more complicated, if you can define it in terms of a ctypes.Structure, you can use https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes, but this still means that any references between objects have to be indirect. (For example, you often have to store indices into an array.) (Of course none of this is bad news if you're using NumPy, because you're probably already storing most of your data in NumPy arrays of simple values, which are sharable.)
Second, shared data are of course subject to race conditions. And, unlike multithreading within a single process, you can't rely on the GIL to help protect you here; there are multiple interpreters that can all be trying to modify the same data at the same time. So you have to use locks or conditions to protect things.

For multiprocessing pipeline check out MPipe.
For shared memory (specifically NumPy arrays) check out numpy-sharedmem.
I've used these to do high-performance realtime, parallel image processing (average accumulation and face detection using OpenCV) while squeezing out all available resources from a multi-core CPU system. Check out Sherlock if interested. Hope this helps.

One option is to use something like brain-plasma that maintains a shared-memory object namespace that is independent of the Python process or thread. Kind of like Redis but can be used with big objects and has a simple API, built on top of Apache Arrow.
$ pip install brain-plasma
# process 1
from brain_plasma import Brain
brain = Brain()
brain['myvar'] = 657
# process 2
from brain_plasma import Brain
brain = Brain()
brain['myvar']
# >>> 657

Python 3.8 now offers shared memory access between processes using multiprocessing.shared_memory. All you hand off between processes is a string that references the shared memory block. In the consuming process you get a memoryview object which supports slicing without copying the data like byte arrays do. If you are using numpy it can reference the memory block in an O(1) operation, allowing fast transfers of large blocks of numeric data. As far as I understand generic objects still need to be deserialized since a raw byte array is what's received by the consuming process.

Why does python multiprocessing pickle objects to pass objects between processes?

Why does the multiprocessing package for python pickle objects to pass them between processes, i.e. to return results from different processes to the main interpreter process? This may be an incredibly naive question, but why can't process A say to process B "object x is at point y in memory, it's yours now" without having to perform the operation necessary to represent the object as a string.

multiprocessing runs jobs in different processes. Processes have their own independent memory spaces, and in general cannot share data through memory.
To make processes communicate, you need some sort of channel. One possible channel would be a "shared memory segment", which pretty much is what it sounds like. But it's more common to use "serialization". I haven't studied this issue extensively but my guess is that the shared memory solution is too tightly coupled; serialization lets processes communicate without letting one process cause a fault in the other.
When data sets are really large, and speed is critical, shared memory segments may be the best way to go. The main example I can think of is video frame buffer image data (for example, passed from a user-mode driver to the kernel or vice versa).
http://en.wikipedia.org/wiki/Shared_memory
http://en.wikipedia.org/wiki/Serialization
Linux, and other *NIX operating systems, provide a built-in mechanism for sharing data via serialization: "domain sockets" This should be quite fast.
http://en.wikipedia.org/wiki/Unix_domain_socket
Since Python has pickle that works well for serialization, multiprocessing uses that. pickle is a fast, binary format; it should be more efficient in general than a serialization format like XML or JSON. There are other binary serialization formats such as Google Protocol Buffers.
One good thing about using serialization: it's about the same to share the work within one computer (to use additional cores) or to share the work between multiple computers (to use multiple computers in a cluster). The serialization work is identical, and network sockets work about like domain sockets.
EDIT: #Mike McKerns said, in a comment below, that multiprocessing can use shared memory sometimes. I did a Google search and found this great discussion of it: Python multiprocessing shared memory