distwq 1.1.0

Distributed queue operations with mpi4py

distwq

Distributed work queue operations using mpi4py.

Allows for easy parallelization in controller/worker mode with one controller submitting function or method calls to workers. Supports multiple ranks per worker (collective workers). Uses mpi4py if available, otherwise processes calls sequentially in one process.

Based on mpi.py from the pyunicorn project.

EXAMPLE

# Example of using distributed work queue distwq
# PYTHONPATH must include the directories in which distwq and this file are located.

import distwq
import numpy as np  
import scipy
from scipy import signal

def do_work(freq):
    fs = 10e3
    N = 1e5
    amp = 2*np.sqrt(2)
    freq = float(freq)
    noise_power = 0.001 * fs / 2
    time = np.arange(N) / fs
    x = amp*np.sin(2*np.pi*freq*time)
    x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
    f, pdens = signal.periodogram(x, fs)
    return f, pdens


def main(controller):
    n = 150
    for i in range(0, n):
        controller.submit_call("do_work", (i+1,), module_name="example_distwq")
    s = []
    for i in range(0, n):
        s.append(controller.get_next_result())
    print("results length : %d" % len(s))
    print(s)
    controller.info()

if __name__ == '__main__':
    if distwq.is_controller:
        distwq.run(fun_name="main", verbose=True, nprocs_per_worker=3)
    else:
        distwq.run(fun_name=None, verbose=True, nprocs_per_worker=3)

API

MPIController

submit_call

 submit_call(name_to_call, args=(), kwargs={}, module_name="__main__", time_est=1, task_id=None)

Submit a call for parallel execution.

If called by the controller and workers are available, the call is submitted to a worker for asynchronous execution.

If called by a worker or if no workers are available, the call is instead executed synchronously on this MPI node.

Examples:

1. Provide ids and time estimate explicitly:

for n in range(0,10):
distwq.submit_call("doit", (n,A[n]), id=n, time_est=n**2)

for n in range(0,10):
result[n] = distwq.get_result(n)

2. Use generated ids stored in a list:

for n in range(0,10):
ids.append(distwq.submit_call("doit", (n,A[n])))

for n in range(0,10):
results.append(distwq.get_result(ids.pop()))

3. Ignore ids altogether:

for n in range(0,10):
distwq.submit_call("doit", (n,A[n]))

for n in range(0,10):
results.append(distwq.get_next_result())

4. Call a module function and use keyword arguments:

distwq.submit_call("solve", (), {"a":a, "b":b},
module="numpy.linalg")

• name_to_call (str): name of callable object (usually a function or static method of a class) as contained in the namespace specified by module.
• args (tuple): the positional arguments to provide to the callable object. Tuples of length 1 must be written (arg,). Default: ()
• kwargs (dict): the keyword arguments to provide to the callable object. Default: {}
• module (str): optional name of the imported module or submodule in whose namespace the callable object is contained. For objects defined on the script level, this is "main", for objects defined in an imported package, this is the package name. Must be a key of the dictionary sys.modules (check there after import if in doubt). Default: "main"
• time_est (float): estimated relative completion time for this call; used to find a suitable worker. Default: 1
• id (int or None): unique id for this call. Must be a possible dictionary key. If None, a random id is assigned and returned. Can be re-used after get_result() for this is. Default: None
• worker: int > 0 and < comm.size, or None: optional no. of worker to assign the call to. If None, the call is assigned to the worker with the smallest current total time estimate. Default: None

Returns:

id of call, to be used in get_result().

get_result

 get_result(task_id)

Returns result of earlier submitted call.

Can only be called by the controller.

If the call is not yet finished, waits for it to finish. Results should be collected in the same order as calls were submitted. For each worker, the results of calls assigned to that worker must be collected in the same order as those calls were submitted. Can only be called once per call.

• id (int) : id of an earlier submitted call, as provided to or returned by submit_call().

Returns:

return value of call.

get_next_result

 get_next_result()

Returns result of next earlier submitted call whose result has not yet been obtained.

Can only be called by the controller.

If the call is not yet finished, waits for it to finish.

Returns:

id, return value of call, or None of there are no more calls in the queue.

info

 info()

Print processing statistics.

Can only be called by the controller.

exit

 exit()

Tells all workers to exit.

Can only be called by the controller.

abort

 abort()

Abort execution on all MPI nodes immediately.

Can be called by controller and workers.

MPIWorker

serve

 serve()

Serves submitted calls until told to finish.

Call this function if workers need to perform initialization different from the controller, like this:

 def workerfun(worker):
     do = whatever + initialization - is * necessary
     worker.serve()
     do = whatever + cleanup - is * necessary

If workerfun() is not defined, serve() will be called automatically by run().

MPICollectiveBroker

serve

 serve()

Broker and serve submitted calls until told to finish. A task is received from the controller and sent to all collective workers associated with this broker via scatter.

Call this function if workers need to perform initialization different from the controller, like this:

def workerfun(worker):
    do = whatever + initialization - is * necessary
    worker.serve()
     do = whatever + cleanup - is * necessary

If workerfun() is not defined, serve() will be called automatically by run().

Procedures

run

run(fun_name=None, module_name='__main__', verbose=False, worker_grouping_method=None, nprocs_per_worker=1, broker_is_worker=False, args=())

Runs in controller/worker mode until fun(controller/worker) finishes.

Must be called on all MPI nodes.

On the controller, run() calls fun_name() and returns when fun_name() returns.

On each worker, run() calls fun() if that is defined, or calls serve() otherwise, and returns when fun() returns, or when fun() returns on the controller, or when controller calls exit().

• module_name (string): module where fun_name is located
• verbose (bool): whether processing information should be printed.
• worker_grouping_method (str): whether to separate worker processes into groups via MPI_Comm_Spawn ("spawn") or MPI_Comm_Split ("split")
• nprocs_per_worker (int): how many processes per worker
• broker_is_worker (bool): when worker_grouping_method is "spawn" or "split" and nprocs_per_worker > 1, MPI_Comm_Spawn or MPI_Comm_split will be used to create workers, and a CollectiveBroker object is used to relay tasks and results between controller and worker. When broker_is_worker is true, the broker also participates in serving tasks, otherwise it only relays calls.
• args (tuple): additional args to pass to fun