multithreading - On what CPU cores are my Python processes running?

Question

The setup

I have written a pretty complex piece of software in Python (on a Windows PC). My software starts basically two Python interpreter shells. The first shell starts up (I suppose) when you double click the main.py file. Within that shell, other threads are started in the following way:

    # Start TCP_thread
    TCP_thread = threading.Thread(name = 'TCP_loop', target = TCP_loop, args = (TCPsock,))
    TCP_thread.start()

    # Start UDP_thread
    UDP_thread = threading.Thread(name = 'UDP_loop', target = UDP_loop, args = (UDPsock,))
    TCP_thread.start()

The Main_thread starts a TCP_thread and a UDP_thread. Although these are separate threads, they all run within one single Python shell.

The Main_threadalso starts a subprocess. This is done in the following way:

p = subprocess.Popen(['python', mySubprocessPath], shell=True)

From the Python documentation, I understand that this subprocess is running simultaneously (!) in a separate Python interpreter session/shell. The Main_threadin this subprocess is completely dedicated to my GUI. The GUI starts a TCP_thread for all its communications.

I know that things get a bit complicated. Therefore I have summarized the whole setup in this figure:

---

I have several questions concerning this setup. I will list them down here:

Question 1 [Solved]

Is it true that a Python interpreter uses only one CPU core at a time to run all the threads? In other words, will the Python interpreter session 1 (from the figure) run all 3 threads (Main_thread, TCP_thread and UDP_thread) on one CPU core?

Answer: yes, this is true. The GIL (Global Interpreter Lock) ensures that all threads run on one CPU core at a time.

Question 2 [Not yet solved]

Do I have a way to track which CPU core it is?

Question 3 [Partly solved]

For this question we forget about threads, but we focus on the subprocess mechanism in Python. Starting a new subprocess implies starting up a new Python interpreter instance. Is this correct?

Answer: Yes this is correct. At first there was some confusion about whether the following code would create a new Python interpreter instance:

    p = subprocess.Popen(['python', mySubprocessPath], shell = True)

The issue has been clarified. This code indeed starts a new Python interpreter instance.

Will Python be smart enough to make that separate Python interpreter instance run on a different CPU core? Is there a way to track which one, perhaps with some sporadic print statements as well?

Question 4 [New question]

The community discussion raised a new question. There are apparently two approaches when spawning a new process (within a new Python interpreter instance):

    # Approach 1(a)
    p = subprocess.Popen(['python', mySubprocessPath], shell = True)

    # Approach 1(b) (J.F. Sebastian)
    p = subprocess.Popen([sys.executable, mySubprocessPath])

    # Approach 2
    p = multiprocessing.Process(target=foo, args=(q,))

The second approach has the obvious downside that it targets just a function - whereas I need to open up a new Python script. Anyway, are both approaches similar in what they achieve?

question from:https://stackoverflow.com/questions/36795086/on-what-cpu-cores-are-my-python-processes-running

Answer 1

Q: Is it true that a Python interpreter uses only one CPU core at a time to run all the threads?

No. GIL and CPU affinity are unrelated concepts. GIL can be released during blocking I/O operations, long CPU intensive computations inside a C extension anyway.

If a thread is blocked on GIL; it is probably not on any CPU core and therefore it is fair to say that pure Python multithreading code may use only one CPU core at a time on CPython implementation.

Q: In other words, will the Python interpreter session 1 (from the figure) run all 3 threads (Main_thread, TCP_thread and UDP_thread) on one CPU core?

I don't think CPython manages CPU affinity implicitly. It is likely relies on OS scheduler to choose where to run a thread. Python threads are implemented on top of real OS threads.

Q: Or is the Python interpreter able to spread them over multiple cores?

To find out the number of usable CPUs:

>>> import os
>>> len(os.sched_getaffinity(0))
16

Again, whether or not threads are scheduled on different CPUs does not depend on Python interpreter.

Q: Suppose that the answer to Question 1 is 'multiple cores', do I have a way to track on which core each thread is running, perhaps with some sporadic print statements? If the answer to Question 1 is 'only one core', do I have a way to track which one it is?

I imagine, a specific CPU may change from one time-slot to another. You could look at something like /proc/<pid>/task/<tid>/status on old Linux kernels. On my machine, task_cpu can be read from /proc/<pid>/stat or /proc/<pid>/task/<tid>/stat:

>>> open("/proc/{pid}/stat".format(pid=os.getpid()), 'rb').read().split()[-14]
'4'

For a current portable solution, see whether psutil exposes such info.

You could restrict the current process to a set of CPUs:

os.sched_setaffinity(0, {0}) # current process on 0-th core

Q: For this question we forget about threads, but we focus on the subprocess mechanism in Python. Starting a new subprocess implies starting up a new Python interpreter session/shell. Is this correct?

Yes. subprocess module creates new OS processes. If you run python executable then it starts a new Python interpeter. If you run a bash script then no new Python interpreter is created i.e., running bash executable does not start a new Python interpreter/session/etc.

Q: Supposing that it is correct, will Python be smart enough to make that separate interpreter session run on a different CPU core? Is there a way to track this, perhaps with some sporadic print statements as well?

See above (i.e., OS decides where to run your thread and there could be OS API that exposes where the thread is run).

multiprocessing.Process(target=foo, args=(q,)).start()

multiprocessing.Process also creates a new OS process (that runs a new Python interpreter).

In reality, my subprocess is another file. So this example won't work for me.

Python uses modules to organize the code. If your code is in another_file.py then import another_file in your main module and pass another_file.foo to multiprocessing.Process.

Nevertheless, how would you compare it to p = subprocess.Popen(..)? Does it matter if I start the new process (or should I say 'python interpreter instance') with subprocess.Popen(..)versus multiprocessing.Process(..)?

multiprocessing.Process() is likely implemented on top of subprocess.Popen(). multiprocessing provides API that is similar to threading API and it abstracts away details of communication between python processes (how Python objects are serialized to be sent between processes).

If there are no CPU intensive tasks then you could run your GUI and I/O threads in a single process. If you have a series of CPU intensive tasks then to utilize multiple CPUs at once, either use multiple threads with C extensions such as lxml, regex, numpy (or your own one created using Cython) that can release GIL during long computations or offload them into separate processes (a simple way is to use a process pool such as provided by concurrent.futures).

Q: The community discussion raised a new question. There are apparently two approaches when spawning a new process (within a new Python interpreter instance):

# Approach 1(a)
p = subprocess.Popen(['python', mySubprocessPath], shell = True)

# Approach 1(b) (J.F. Sebastian)
p = subprocess.Popen([sys.executable, mySubprocessPath])

# Approach 2
p = multiprocessing.Process(target=foo, args=(q,))

"Approach 1(a)" is wrong on POSIX (though it may work on Windows). For portability, use "Approach 1(b)" unless you know you need cmd.exe (pass a string in this case, to make sure that the correct command-line escaping is used).

The second approach has the obvious downside that it targets just a function - whereas I need to open up a new Python script. Anyway, are both approaches similar in what they achieve?

subprocess creates new processes, any processes e.g., you could run a bash script. multprocessing is used to run Python code in another process. It is more flexible to import a Python module and run its function than to run it as a script. See Call python script with input with in a python script using subprocess.