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I have decided to learn how multi-threading is done in Python, and I did a comparison to see what kind of performance gain I would get on a dual-core CPU. I found that my simple multi-threaded code actually runs slower than the sequential equivalent, and I cant figure out why.

The test I contrived was to generate a large list of random numbers and then print the maximum

from random import random import threading def ox(): print max([random() for x in xrange(20000000)])

ox() takes about 6 seconds to complete on my Intel Core 2 Duo, while ox();ox() takes about 12 seconds.

I then tried calling ox() from two threads to see how fast that would complete.

def go(): r = threading.Thread(target=ox) r.start() ox()

go() takes about 18 seconds to complete, with the two results printing within 1 second of eachother. Why should this be slower?

I suspect ox() is being parallelized automatically, because I if look at the Windows task manager performance tab, and call ox() in my python console, both processors jump to about 75% utilization until it completes. Does Python automatically parallelize things like max() when it can?

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  1. Python has the GIL. Python bytecode will only be executed by a single processor at a time. Only certain C modules (which don't manage Python state) will be able to run concurrently.
  2. The Python GIL has a huge overhead in locking the state between threads. There are fixes for this in newer versions or in development branches - which at the very least should make multi-threaded CPU bound code as fast as single threaded code.

You need to use a multi-process framework to parallelize with Python. Luckily, the multiprocessing module which ships with Python makes that fairly easy.

Very few languages can auto-parallelize expressions. If that is the functionality you want, I suggest Haskell (Data Parallel Haskell)

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It's worth noting that threading is still useful in Python for certain types of task, such as I/O-bound operations.
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The problem is in function random() If you remove random from you code. Both cores try to access to shared state of the random function. Cores work consequentially and spent a lot of time on caches synchronization. Such behavior is known as false sharing. Read this article False Sharing

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As Yann correctly pointed out, the Python GIL prevents parallelization from happening in this example. You can either use the python multiprocessing module to fix that or if you are willing to use other open source libraries, Ray is also a great option to get around the GIL problem and is easier to use and has more features than the Python multiprocessing library.

This is how you can parallelize your code example with Ray:

from random import random import ray ray.init() @ray.remote def ox(): print(max([random() for x in range(20000000)])) %time x = ox.remote(); y = ox.remote(); ray.get([x, y])

On my machine, the single threaded ox() code you posted takes 1.84s and the two invocations with ray take 1.87s combined, so we get almost perfect parallelization here.

Ray also makes it very efficient to share data between tasks, on a single machine it will use shared memory under the hood, see https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html.

You can also run the same program across different machines on your cluster or the cloud without having to modify the program, see the documentation (https://ray.readthedocs.io/en/latest/using-ray-on-a-cluster.html and https://ray.readthedocs.io/en/latest/autoscaling.html).

Disclaimer: I'm one of the Ray developers.

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