cereggii 0.2.0

Thread synchronization utilities for free-threaded Python.

cereggii

Thread synchronization utilities for free-threaded Python.

This library provides some atomic data types which, in a multithreaded context, are generally more performant compared to CPython's builtin types.

Cereus greggii

The Peniocereus Greggii (also known as Cereus Greggii) is a flower native to Arizona, New Mexico, Texas, and some parts of northern Mexico.

This flower blooms just one summer night every year and in any given area, all these flowers bloom in synchrony.

Wikipedia

Image credits: Patrick Alexander, Peniocereus greggii var. greggii, south of Cooke's Range, Luna County, New Mexico, 10 May 2018, CC0. source

Installing

This library is experimental

Arm disclaimer: aarch64 processors are generally not supported, but this library was successfully used with Apple Silicon.

Using @colesbury's original nogil fork is required to use this library. You can get it with pyenv:

pyenv install nogil-3.9.10-1

Then, you may fetch this library from PyPI:

pip install cereggii

If you happened to use a non-free-threaded interpreter, this library may not be able to run, and if it does, you will see poor performance.

AtomicInt

In Python (be it free-threaded or not), the following piece of code is not thread-safe:

i = 0
i += 1

That is, if i is shared with multiple threads, and they attempt to modify i, the value of i after any number (> 1) of writes, is undefined.

The following piece of code is instead thread-safe:

import cereggii


i = cereggii.AtomicInt(0)
i += 1
print(i.get())

Also, consider the counter example where three counter implementations are compared:

1. using a built-in int,
2. using AtomicInt, and
3. using AtomicInt with AtomicIntHandle.

A counter using the built-in int.
spam.counter=0
spam.counter=5019655
Took 39.17 seconds.

A counter using cereggii.AtomicInt.
spam.counter.get()=0
spam.counter.get()=15000000
Took 36.78 seconds (1.07x faster).

A counter using cereggii.AtomicInt and cereggii.AtomicIntHandle.
spam.counter.get()=0
spam.counter.get()=15000000
Took 2.64 seconds (14.86x faster).

Notice that when using AtomicInt the count is correctly computed, and that using AtomicInt.get_handle to access the counter greatly improves performance. When using AtomicIntHandle, you should see your CPUs being fully used, because no implicit lock prevents the execution of any thread. [^implicitlock]

AtomicInt borrows part of its API from Java's AtomicInteger, so that it should feel familiar to use, if you're coming to Python from Java. It also implements most numeric magic methods, so that it should feel comfortable to use for Pythonistas. It tries to mimic Python's int as close as possible, with some caveats:

• it is bound to 64-bit integers, so you may encounter OverflowError;
• hashing is based on the AtomicInt's address in memory, so two distinct AtomicInts will have distinct hashes, even when they hold the same value (bonus feature: an AtomicIntHandle has the same hash of its corresponding AtomicInt); [^1]
• the following operations are not supported:
  • __itruediv__ (an AtomicInt cannot be used to store floats)
  • as_integer_ratio
  • bit_length
  • conjugate
  • from_bytes
  • to_bytes
  • denominator
  • numerator
  • imag
  • real

[^implicitlock]: Put simply, in a free-threaded build, the global interpreter lock is substituted with many per-object locks.

[^1]: This behavior ensures the hashing property that identity implies hash equality.

An explanation of these claims

First, a += 1 in CPython actually translates to more than one bytecode instruction, namely:

LOAD_CONST               2 (1)
INPLACE_ADD              0 (a)
STORE_FAST               0 (a)

This means that between the INPLACE_ADD and the STORE_FAST instructions, the value of a may have been changed by another thread, so that one or multiple increments may be lost.

Second, the performance problem. How come the speedup?

If we look again at the example code, there are a couple of implicit memory locations which are being contended by threads:

1. the reference count of spam.count;
2. the reference counts of the int objects themselves; and
3. the lock protecting the implicit spam.__dict__.

These contentions are eliminated by AtomicInt, cf.:

1. spam.count is accessed indirectly through an AtomicIntHandle which avoids contention on its reference count (it is contended only during the .get_handle() call);
2. this is avoided by not creating int objects during the increment;
3. again, using the handle instead of the AtomicInt itself avoids spurious contention, because spam.__dict__ is not modified.

Also see colesbury/nogil#121.

AtomicDict

You can see that there is some more performance to be gained by simply using AtomicDict, looking at the execution of the count keys example.

Counting keys using the built-in dict.
Took 35.46 seconds.

Counting keys using cereggii.AtomicDict.
Took 7.51 seconds (4.72x faster).

Notice that the performance gain was achieved by simply wrapping the dictionary initialization with AtomicDict (compare Spam.d and AtomicSpam.d in the example source).

The usage of AtomicInt provides correctness, regardless of the hashmap implementation. But using AtomicDict instead of dict improves performance, even without using handles: writes to distinct keys do not generate contention.

Pre-sized dictionary, with partitioned iterations

AtomicDict provides some more features compared to Python's dict, in the partitioned iteration example two of them are shown:

1. pre-sizing, which allows for the expensive dynamic resizing of a hash table to be avoided, and
2. partitioned iterations, which allows to split the number of elements among threads.

Insertion into builtin dict took 36.81s
Builtin dict iter took 17.56s with 1 thread.
----------
Insertion took 17.17s
Partitioned iter took 8.80s with 1 threads.
Partitioned iter took 5.03s with 2 threads.
Partitioned iter took 3.92s with 3 threads.

AtomicRef

You can use an AtomicRef when you have a shared variable that points to an object, and you need to change the referenced object concurrently.

This is not available in the Python standard library and was initially implemented as part of AtomicDict.

import cereggii


o = object()
d = {}
i = 0

r = cereggii.AtomicRef()
assert r.get() is None
assert r.compare_and_set(None, o)
assert r.get_and_set(d) == o
r.set(i)  # always returns None

Experimental

This library is experimental and should not be used in a production environment.

After all, as of now, it requires a non-official fork in order to run.

Porting to free-threaded Python 3.13 (3.13t) is planned.