To use multiple processors in CPython your only choice is the multiprocessing module. CPython keeps a lock on it's internals (the GIL) which prevents threads on other cpus to work in parallel. The multiprocessing module creates new processes ( like subprocess ) and manages communication between them.

multiprocessing is a great Swiss-army knife type of module. It is more general than threads, as you can even perform remote computations. This is therefore the module I would suggest you use.

The subprocess module would also allow you to launch multiple processes, but I found it to be less convenient to use than the new multiprocessing module.

Threads are notoriously subtle, and, with CPython, you are often limited to one core, with them (even though, as noted in one of the comments, the Global Interpreter Lock (GIL) can be released in C code called from Python code).

I believe that most of the functions of the three modules you cite can be used in a platform-independent way. On the portability side, note that multiprocessing only comes in standard since Python 2.6 (a version for some older versions of Python does exist, though). But it's a great module!

In a similar case I opted for separate processes and the little bit of necessary communication trough network socket. It is highly portable and quite simple to do using python, but probably not the simpler (in my case I had also another constraint: communication with other processes written in C++).

In your case I would probably go for multiprocess, as python threads, at least when using CPython, are not real threads. Well, they are native system threads but C modules called from Python may or may not release the GIL and allow other threads them to run when calling blocking code.

Shell out and let the unix out to do your jobs:

use iterpipes to wrap subprocess and then:

From Ted Ziuba's site

INPUTS_FROM_YOU | xargs -n1 -0 -P NUM ./process #NUM parallel processes

OR

Gnu Parallel will also serve

You hang out with GIL while you send the backroom boys out to do your multicore work.

For me this is actually pretty simple:

The subprocess option:

subprocess is for running other executables --- it's basically a wrapper around os.fork() and os.execve() with some support for optional plumbing (setting up PIPEs to and from the subprocesses. Obviously you could use other inter-process communications (IPC) mechanisms, such as sockets, or Posix or SysV shared memory. But you're going to be limited to whatever interfaces and IPC channels are supported by the programs you're calling.

Commonly, one uses any subprocess synchronously --- simply calling some external utility and reading back its output or awaiting its completion (perhaps reading its results from a temporary file, or after it's posted them to some database).

However one can spawn hundreds of subprocesses and poll them. My own personal favorite utility classh does exactly that. The biggest disadvantage of the subprocess module is that I/O support is generally blocking. There is a draft PEP-3145 to fix that in some future version of Python 3.x and an alternative asyncproc (Warning that leads right to the download, not to any sort of documentation nor README). I've also found that it's relatively easy to just import fcntl and manipulate your Popen PIPE file descriptors directly --- though I don't know if this is portable to non-UNIX platforms.

(Update: 7 August 2019: Python 3 support for ayncio subprocesses: asyncio Subprocessses)

subprocess has almost no event handling support ... though you can use the signal module and plain old-school UNIX/Linux signals --- killing your processes softly, as it were.

The multiprocessing option:

multiprocessing is for running functions within your existing (Python) code with support for more flexible communications among this family of processes. In particular it's best to build your multiprocessing IPC around the module's Queue objects where possible, but you can also use Event objects and various other features (some of which are, presumably, built around mmap support on the platforms where that support is sufficient).

Python's multiprocessing module is intended to provide interfaces and features which are very similar to threading while allowing CPython to scale your processing among multiple CPUs/cores despite the GIL (Global Interpreter Lock). It leverages all the fine-grained SMP locking and coherency effort that was done by developers of your OS kernel.

The threading option:

threading is for a fairly narrow range of applications which are I/O bound (don't need to scale across multiple CPU cores) and which benefit from the extremely low latency and switching overhead of thread switching (with shared core memory) vs. process/context switching. On Linux this is almost the empty set (Linux process switch times are extremely close to its thread-switches).

threading suffers from two major disadvantages in Python.

One, of course, is implementation specific --- mostly affecting CPython. That's the GIL. For the most part, most CPython programs will not benefit from the availability of more than two CPUs (cores) and often performance will suffer from the GIL locking contention.

The larger issue which is not implementation specific, is that threads share the same memory, signal handlers, file descriptors and certain other OS resources. Thus the programmer must be extremely careful about object locking, exception handling and other aspects of their code which are both subtle and which can kill, stall, or deadlock the entire process (suite of threads).

By comparison the multiprocessing model gives each process its own memory, file descriptors, etc. A crash or unhandled exception in any one of them will only kill that resource and robustly handling the disappearance of a child or sibling process can be considerably easier than debugging, isolating and fixing or working around similar issues in threads.

• (Note: use of threading with major Python systems, such as NumPy, may suffer considerably less from GIL contention than most of your own Python code would. That's because they've been specifically engineered to do so; the native/binary portions of NumPy, for example, will release the GIL when that's safe).

The twisted option:

It's also worth noting that Twisted offers yet another alternative which is both elegant and very challenging to understand. Basically, at the risk of over simplifying to the point where fans of Twisted may storm my home with pitchforks and torches, Twisted provides event-driven co-operative multi-tasking within any (single) process.

To understand how this is possible one should read about the features of select() (which can be built around the select() or poll() or similar OS system calls). Basically it's all driven by the ability to make a request of the OS to sleep pending any activity on a list of file descriptors or some timeout.

Awakening from each of these calls to select() is an event --- either one involving input available (readable) on some number of sockets or file descriptors, or buffering space becoming available on some other (writable) descriptors or sockets, some exceptional conditions (TCP out-of-band PUSH'd packets, for example), or a TIMEOUT.

Thus the Twisted programming model is built around handling these events then looping on the resulting "main" handler, allowing it to dispatch the events to your handlers.

I personally think of the name, Twisted as evocative of the programming model ... since your approach to the problem must be, in some sense, "twisted" inside out. Rather than conceiving of your program as a series of operations on input data and outputs or results, you're writing your program as a service or daemon and defining how it reacts to various events. (In fact the core "main loop" of a Twisted program is (usually? always?) a reactor()).

The major challenges to using Twisted involve twisting your mind around the event driven model and also eschewing the use of any class libraries or toolkits which are not written to co-operate within the Twisted framework. This is why Twisted supplies its own modules for SSH protocol handling, for curses, and its own subprocess/Popen functions, and many other modules and protocol handlers which, at first blush, would seem to duplicate things in the Python standard libraries.

I think it's useful to understand Twisted on a conceptual level even if you never intend to use it. It may give insights into performance, contention, and event handling in your threading, multiprocessing and even subprocess handling as well as any distributed processing you undertake.

(Note: Newer versions of Python 3.x are including asyncio (asynchronous I/O) features such as async def, the @async.coroutine decorator, and the await keyword, and yield from future support. All of these are roughly similar to Twisted from a process (co-operative multitasking) perspective). (For the current status of Twisted support for Python 3, check out: https://twistedmatrix.com/documents/current/core/howto/python3.html)

The distributed option:

Yet another realm of processing you haven't asked about, but which is worth considering, is that of distributed processing. There are many Python tools and frameworks for distributed processing and parallel computation. Personally I think the easiest to use is one which is least often considered to be in that space.

It is almost trivial to build distributed processing around Redis. The entire key store can be used to store work units and results, Redis LISTs can be used as Queue() like object, and the PUB/SUB support can be used for Event-like handling. You can hash your keys and use values, replicated across a loose cluster of Redis instances, to store the topology and hash-token mappings to provide consistent hashing and fail-over for scaling beyond the capacity of any single instance for co-ordinating your workers and marshaling data (pickled, JSON, BSON, or YAML) among them.

Of course as you start to build a larger scale and more sophisticated solution around Redis you are re-implementing many of the features that have already been solved using, Celery, Apache Spark and Hadoop, Zookeeper, etcd, Cassandra and so on. Those all have modules for Python access to their services.

[Update: A couple of resources for consideration if you're considering Python for computationally intensive across distributed systems: IPython Parallel and PySpark. While these are general purpose distributed computing systems, they are particularly accessible and popular subsystems data science and analytics].

Conclusion

There you have the gamut of processing alternatives for Python, from single threaded, with simple synchronous calls to sub-processes, pools of polled subprocesses, threaded and multiprocessing, event-driven co-operative multi-tasking, and out to distributed processing.

I'm not sure what the program is going to do I came across this thread while trying to run an API load test, which was using requests. All suggestions above were very detailed.

In the end the best result was to use grequests or a which quickly devolved into aiohttp.

grequests: Asynchronous Requests with Python requests

aiohttp starter: https://pythonalgos.com/send-api-requests-asynchronously-in-python/