bocpy 0.2.2

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 processes.

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] the bocpy library depends on the Cross-Interpreter Data mechanism, which was introduced in Python 3.7. We explicitly test and provide wheels for all versions of Python that have not been end-of-lifed (3.10.19 as of time of writing is the oldest version we support). The library may not work in older versions of Python.

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 underlying BOC scheduler 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.

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 processes.

This library

Our implementation is built on top of the subinterpreters mechanism and the Cross-Interpreter Data, or XIData, API. As of Python 3.12, sub-interpreters have their own GIL and thus run in parallel, and thus BOC will also run fully in parallel.

In addition the providing 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 subinterpreters. See the bocpy-primes and bocpy-calculator examples for the usage of these lower-level functions.

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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