cereggii 0.1.0

Concurrent threading utilities for 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

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:

a = 0
a += 1

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

The following piece of code is instead thread-safe:

a = cereggii.AtomicInt(0)
a += 1

Also, consider the following piece of code:

import threading


class Spam:
    def __init__(self):
        self.counter = 0


spam = Spam()
print(f"{spam.counter=}")


def incr():
    for i in range(1_000_000):
        spam.counter += 1


t1 = threading.Thread(target=incr)
t2 = threading.Thread(target=incr)
t1.start()
t2.start()
t1.join()
t2.join()

print(f"{spam.counter=}")

The output you'll see onscreen is not known. If you subsititute self.counter = 0 with self.counter = AtomicInt(0), you'll be guaranteed to see 2_000_000, and the program should run slightly faster.

If you make an additional modification, your program will run much faster:

def incr():
    h = spam.counter.get_handle()

    for i in range(1_000_000):
        h += 1

When using AtomicIntHandle, you should see your CPUs being fully used.

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

[^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:

              0 LOAD_CONST               2 (1)
              2 INPLACE_ADD              0 (a)
              4 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 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.

Also see colesbury/nogil#121.

AtomicDict

Currently, the implementation of AtomicDict is quite limited:

• (dpdani/cereggii#3) it can hold at most $2^{25}$ keys (~33.5M);
• (dpdani/cereggii#4) it does not support deletions (del d[k]);
• (dpdani/cereggii#5) it does not support dynamic resizing; and
• several common functionalities are missing.

You can see that there is some more performance to be gained by simply using AtomicDict, comparing the following two programs.

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.

With dict:

import threading

from cereggii import AtomicDict, AtomicInt


class Spam:
    def __init__(self):
        self.d = {k: AtomicInt(0) for k in range(10)}


spam = Spam()
print(
    f"{spam.d[0].get()=} {spam.d[1].get()=} {spam.d[2].get()=} {spam.d[3].get()=} {spam.d[4].get()=} "
    f"{spam.d[5].get()=} {spam.d[6].get()=} {spam.d[7].get()=} {spam.d[8].get()=} {spam.d[9].get()=} "
)


def incr():
    keys = list(range(10))
    for _ in range(5_000_000):
        spam.d[keys[_ % len(keys)]] += 1


threads = [threading.Thread(target=incr) for _ in range(3)]
for t in threads:
    t.start()

for t in threads:
    t.join()

print(
    f"{spam.d[0].get()=} {spam.d[1].get()=} {spam.d[2].get()=} {spam.d[3].get()=} {spam.d[4].get()=} "
    f"{spam.d[5].get()=} {spam.d[6].get()=} {spam.d[7].get()=} {spam.d[8].get()=} {spam.d[9].get()=} "
)

To use AtomicDict, substitute in self.d = AtomicDict({k: AtomicInt(0) for k in range(10)}).

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.