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Pool

Pool is a library on top of Charm4py that can schedule sets of "tasks" among the available hosts and processors. Tasks can also spawn other tasks. A task is simply a Python function. There is only one pool that is shared among all processes. Any tasks, regardless of when or where they are spawned, will use the same pool of distributed workers, thereby avoiding unnecessary costs like process creation or creating more processes than processors.

Warning

charm.pool is experimental, the API and performance (especially at large scales) is still subject to change.

Note

The current implementation of charm.pool reserves process 0 for a scheduler. This means that if you are running Charm4py with N processes, there will be N-1 pool workers, and thus N-1 is the maximum speedup using the pool. You might want to adjust the number of processes accordingly.

The pool can be used at any point after the application has started, and can be used from any process. Note that there is no limit to the amount of "jobs" that can be sent to the pool at the same time.

The API of charm.pool is:

  • map(func, iterable, chunksize=1, ncores=-1)

    This is a parallel equivalent of the map function, which applies the function func to every item of iterable, returning the list of results. It divides the iterable into a number of chunks, based on the chunksize parameter, and submits them to the pool, each as a separate task. This method blocks the current coroutine until the result arrives.

    The parameter ncores limits the job to use a specified number of cores. If this value is negative, the pool will use all available cores (note that the total number of available cores is determined at application launch).

    Use the @coro decorator on your functions if you want them to be able to suspend (for example, if they create other tasks and need to wait for the results).

  • map_async(func, iterable, chunksize=1, ncores=-1)

    This is the same as the previous method but immediately returns a :ref:`Future <futures-api-label>`, which can be queried asynchronously.

  • Task(func, args, ret=False, awaitable=False)

    Create a single task to run the function func. The function will receive args as unpacked arguments.

    By default this returns nothing. If awaitable is True, the call returns a :ref:`Future <futures-api-label>`, which can be used to wait for completion of the task. If ret is True, the call returns a :ref:`Future <futures-api-label>`, which can be used to wait for the task's return value.

    Creating a single task is similar to using map_async(func, iterable) with an iterable of length one. There are, however, some subtle differences:

    • By default it doesn't create a future or receive a result, which is less expensive.
    • The task can spawn other tasks without having to be a coroutine (if it doesn't request a future).
    • The task receives the arguments unpacked.

Examples

from charm4py import charm

def square(x):
    return x**2

def main(args):
    result = charm.pool.map(square, range(10), chunksize=2)
    print(result)  # prints [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
    exit()

charm.start(main)

Note that due to communication and other overheads, grouping items into chunks (with chunksize) is necessary for best efficiency when the duration of tasks is very small (e.g. less than one millisecond). How small a task size (aka grain size) the pool can efficiently support depends on the actual overhead, which depends on communication performance (network speed, communication layer used -TCP, MPI, etc-, number of hosts...). The chunksize parameter can be used to automatically increase the grainsize.

from charm4py import charm, coro

# Recursive Parallel Fibonacci

@coro
def fib(n):
    if n < 2:
        return n
    return sum(charm.pool.map(fib, [n-1, n-2]))

def main(args):
    print('fibonacci(13)=', fib(13))
    exit()

charm.start(main)
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