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from multiprocessing import Process, Queue

def reduce(que, data):
    que.put(sum(data))

def calc_mean_serial(data):
    return sum(data) / len(data)

def calc_mean_parallel(data, num_procs=8):
    sum = 0
    if __name__ == '__main__':
        chunk = (int) (len(data) / num_procs)
        procs = []
        q = Queue()

        for i in range(num_procs):
            p = Process(target=reduce, args=(q, data[i*chunk:i*chunk+chunk if i != num_procs-1 else None]))
            p.start()
            procs.append(p)

        for p in procs:
            p.join()

        while not q.empty():
            sum = sum + q.get()

    return sum / len(data)

print("serial", calc_mean_serial([i for i in range(1,100000)]))
print("parallel", calc_mean_parallel([i for i in range(1,100000)]))

Code: Alles auswählen

serial 50000.0
parallel 50000.0

Code: Alles auswählen

serial 50000.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
serial 50000.0
parallel 0.0
parallel 50000.0

Code: Alles auswählen

from multiprocessing import Process, Queue

def reduce(queue, data):
    queue.put(sum(data))

def calc_mean_serial(data):
    return sum(data) / len(data)

def calc_mean_parallel(data, num_procs=8):
    chunk = len(data) // num_procs
    queue = Queue()

    processes = []
    for i in range(num_procs):
        process = Process(target=reduce, args=(queue, data[i*chunk:i*chunk+chunk if i != num_procs-1 else None]))
        process.start()
        processes.append(process)

    for process in processes:
        process.join()

    total = 0
    while not queue.empty():
        total += queue.get()
    return total / len(data)

if __name__ == '__main__':
    print("serial", calc_mean_serial([i for i in range(1,100000)]))
    print("parallel", calc_mean_parallel([i for i in range(1,100000)]))