bocpy 0.5.0

bocpy is a Python extension that adds Behavior-oriented concurrency built on top of cross-interpreter data.

bocpy

Behavior-Oriented Concurrency (BOC) is a new paradigm for parallel and concurrent programming which is particularly well-suited to Python. In a BOC program, data is shared such that each behavior has unique temporal ownership of the data, removing the need for locks to coordinate access. For Python programmers, this brings a lot of benefits. Behaviors are implemented as decorated functions, and from the programmer's perspective, those functions work like normal. Importantly, the programmer's task shifts from solving concurrent data access problems to organizing data flow through functions. The resulting programs are easier to understand, easier to support, easier to extend, and unlock multi-core performance due to the ability to schedule behaviors to run efficiently across multiple sub-interpreters.

BOC has been implemented in several languages, including as a foundational aspect of the research language Verona, and now has been implemented in Python.

Getting Started

You can install bocpy via PyPi:

pip install bocpy

We provide pre-compiled wheels for Python 3.10 onwards on most platforms, but if you have problems with your particular platform/version combination, please file an issue on this repository.

[!NOTE] We provide wheels for Python 3.10 and newer, but bocpy only achieves true parallelism on Python 3.12+, where each sub-interpreter has its own GIL. On 3.10 and 3.11 behaviors still run, but they are serialised by the global GIL. The library may not work on Python versions older than 3.10.

Python version support

The mainline (main) branch in the diagram is the standard CPython build:

• 3.10 / 3.11 — wheels are published and @when works, but every sub-interpreter still shares one process-wide GIL, so behaviors execute one at a time. Use these versions for portability rather than performance.
• 3.12+ — each sub-interpreter gets its own GIL (PEP 684), so worker behaviors run in parallel across cores. This is where bocpy delivers on its concurrency story.
• 3.14 is the current default development and CI target; 3.15 is validated as it stabilises.

The free-threaded branch tracks the no-GIL CPython builds (informally "3.13t", "3.14t", "3.15t" — see PEP 703). bocpy runs unmodified on these interpreters today: we don't re-enable the GIL, and the cown / 2PL protocol gives the same data-race-free guarantees you get on the GIL build. The catch is overhead — on free-threaded Python, the sub-interpreter and XIData machinery is pure ceremony, since plain threads in the main interpreter would already run in parallel.

Issue #5 tracks adding an alternative direct-threading backend that detects a free-threaded interpreter at runtime and skips the sub-interpreter / transpiler / XIData path entirely, while keeping the public Cown / @when API unchanged. We're holding off on that work until the free-threaded build and the relevant CPython APIs stabilise.

Scaling with cores

The chart below shows BOC runtime throughput as the worker count grows from 1 to 8, plotted as speedup relative to a single worker. Numbers come from examples/benchmark.py — a chain-ring workload that exercises the scheduler, two-phase locking, sub-interpreter crossings and the message queue together — run on CPython 3.14 (mean of 3 repeats, 8 s each).

The line is the ideal y = x reference; the bars are measured speedup. Up to 8 workers, BOC delivers roughly linear scaling on this microbenchmark (≈7.5× at 8 workers). Real applications carry serial costs that this benchmark deliberately strips out — see the docstring at the top of examples/benchmark.py for the load-bearing caveats. To reproduce:

python examples/benchmark.py \
    --sweep-axis workers --sweep-values 1,2,3,4,5,6,7,8 \
    --duration 8 --warmup 2 --repeats 3 \
    --output scaling.json

A behavior can be thought of as a function which depends on zero or more concurrently-owned data objects (which we call cowns). As a programmer, you indicate that you want the function to be called once all of those resources are available. For example, let's say that you had two complex and time-consuming operations, and you needed to act on the basis of both of their outcomes:

def buy_cheese():
    logger = logging.getLogger("cheese_shop")
    for name in all_known_cheeses():
        if is_available(logger, name):
            return name
    
    cleanup_shop(logger)
    return None


def order_meal(exclude: str):
    logger = logging.getLogger("greasy_spoon")
    for dish in menu():
        logger.info(dish)
        if exclude.lower() not in dish.lower():
            logger.info(f"That doesn't have much {exclude} in it")
            return dish

        vikings(logger)
        if random.random() < 0.3:
            logger.info("<bloody vikings>")

    return None


cheese = buy_cheese()
meal = order_meal(exclude="spam")

if meal is not None:
    eat(meal)
elif cheese is not None:
    eat(cheese)

if meal is not None:
    print("I really wanted some cheese...")
elif cheese is not None:
    print("Cheesy comestibles")

return_to_library()

The code above will work, but requires the purveying of cheese and the navigation of the menu for non-spam options to happen sequentially. If we wanted to do these tasks in parallel, we will end up with some version of nested waiting, which can result in deadlock. With BOC, we would write the above like this:

from bocpy import wait, when, Cown

# ...

def buy_cheese():
    cheese = Cown(None)

    @when(cheese)
    def _(cheese):
        logger = logging.getLogger("cheese_shop")
        for name in all_known_cheeses():
            if is_available(logger, name):
                cheese.value = name
                return

        cleanup_shop(logger)

    return cheese


def order_meal(exclude: str):
    order = Cown(None)

    @when(order)
    def _(order):
        logger = logging.getLogger("greasy_spoon")
        logger.info("We have...")
        for dish in menu():
            logger.info(dish)
            if exclude.lower() not in dish.lower():
                logger.info(f"That doesn't have much {exclude} in it")
                order.value = dish
                return

            vikings(logger)
            if random.random() < 0.3:
                logger.info("<bloody vikings>")

    return order


cheese = buy_cheese()
meal = order_meal(exclude="spam")


@when(cheese, meal)
def _(cheese, meal):
    if meal.value is not None:
        eat(meal.value)
    elif cheese.value is not None:
        eat(cheese.value)
    else:
        print("<stomach rumbles>")


@when(cheese, meal)
def _(cheese, meal):
    if meal.value is not None:
        print("I really wanted cheese...")
    elif cheese.value is not None:
        print("Cheesy comestibles!")

    return_to_library()


wait()

You can view the full example here

The BOC runtime ensures that this operates without deadlock, by construction.

Examples

We provide a few examples to show different ways of using BOC in a program:

1. bocpy-bank: Shows an example where two objects (in this case, bank accounts), interact in an atomic way.
2. bocpy-dining-philosophers: The classic Dining Philosphers problem implemented using BOC.
3. bocpy-fibonacci: A parallel implementation of Fibonacci calculation.
4. bocpy-cooking-boc: The example from the BOC tutorial.
5. bocpy-boids: An agent-based bird flocking example demonstrating the Matrix class to do distributed computation over cores. Note: you'll need to install pyglet first in order to run the bocpy-boids example.
6. bocpy-primes and bocpy-prime-factor: parallel prime sieve and Pollard's rho factorisation, the latter coordinating early termination via the noticeboard.
7. bocpy-calculator: a small Erlang-style calculator service driven by send/receive.
8. bocpy-cooking-threads: the cooking example written with plain threads, for comparison with bocpy-cooking-boc.
9. bocpy-sketches: the cheese-and-spam sketch shown above as a runnable script.

Why BOC for Python?

For many Python programmers, the GIL has established a programming model in which they do not have to think about the many potential issues that are introduced by concurrency, in particular data races. One of the best features of BOC is that, due to the way behaviors interact with concurrently owned data (cowns), each behavior can operate over its data without a need to change this familiar programming model. Even in a free-threading context, BOC will reduce contention on locks and provide programs which are data-race free by construction. Our initial research and experiments with BOC have shown near linear scaling over cores, with up to 32 concurrent worker sub-interpreters.

This library

Our implementation is built on top of the sub-interpreters mechanism and the Cross-Interpreter Data (XIData) API. As of Python 3.12 each sub-interpreter has its own GIL, so behaviors scheduled by bocpy run truly in parallel.

In addition to the when function decorator, the library also exposes low-level Erlang-style send and selective receive functions which enable lock-free communication across threads and sub-interpreters. See the bocpy-primes and bocpy-calculator examples for the usage of these lower-level functions.

For cross-behavior data sharing that does not warrant a Cown, the library also provides a small noticeboard — a global key-value store of up to 64 entries. Behaviors can notice_write, notice_update (atomic read-modify-write) and notice_delete keys without acquiring any cowns, and read a frozen snapshot via noticeboard() / notice_read(). The bocpy-prime-factor example uses it to coordinate early termination across worker behaviors.

Additional Info

BOC is built on a solid foundation of serious scholarship and engineering. For further reading, please see:

1. When Concurrency Matters: Behaviour-Oriented Concurrency
2. Reference implementation in C#
3. OOPSLA23 Talk

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