使用多处理时 python 中的缓慢泡菜转储

Question

所以，我正在尝试使用多处理模块并行化 function，它使用 python 3.7 解决 pyomo 实例。 该代码有效，但启动时间很荒谬（每个进程约 25 秒）。 奇怪的是，我在另一台计算机上尝试了相同的代码，但功能却没有那么强大，它下降到大约 2 秒（相同的代码、相同数量的并行进程、相同的版本，除了 Python，在那台电脑上是 3.6）。

使用cProfile，我发现pickler 的dump 方法是一个耗时的方法，但我似乎无法理解为什么要花这么长时间。 数据很小，我通过使用 sys.getsizeof() 检查并行化 function 的任何 arguments 是否比预期的大，但它们不是。

有谁知道泡菜转储缓慢的原因是什么？

编码：

from pyomo.environ import *
from pyomo.opt import SolverFactory, TerminationCondition
from pyomo.opt.parallel import SolverManagerFactory
import sys
import multiprocessing

def worker(init_nodes[i_nodo][j_nodo], data, optsolver, queue, shared_incumbent_data):
    #[pyomo instances solving and constraining]
    return

def foo(model, data, optsolver, processes = multiprocessing.cpu_count()):

    queue = multiprocessing.Queue()
    process_dict = {}

    for i_node in range(len(init_nodes)): #init_nodes is a list containing lists of pyomo instances
        for j_node in range(len(init_nodes[i_node])):
            
            process_name = str(i_node) + str(j_node)
            print(" - Data size:", sys.getsizeof(data)) #same for all of the args
            
            process_dict[process_name] = multiprocessing.Process(target=worker, args=(init_nodes[i_nodo][j_nodo], data, optsolver, queue, shared_incumbent_data))

            pr = cProfile.Profile()
            pr.enable()                 

            process_dict[process_name].start()

            pr.disable()
            ps = pstats.Stats(pr)
            ps.sort_stats('time').print_stats(5)

    for n_nodo in process_dict:
        process_dict[n_nodo].join(timeout=0)

#imports        
#[model definition]
#[data is obtained from 3 .tab files, the biggest one has a 30 x 40 matrix, with 1 to 3 digit integers]     
optsolver = SolverFactory("gurobi")

if __name__ == "__main__":
    foo(model, data, optsolver, 4)

sys.getsizeof()得到的arguments的大小和第一台计算机上的.start()的profile

 - Data size: 56
 - Init_nodes size: 72
 - Queue size: 56
 - Shared incumbent data size: 56

         7150 function calls (7139 primitive calls) in 25.275 seconds

   Ordered by: internal time
   List reduced from 184 to 5 due to restriction <5>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        2   25.262   12.631   25.267   12.634 {method 'dump' of '_pickle.Pickler' objects}
        1    0.004    0.004    0.004    0.004 {built-in method _winapi.CreateProcess}
     1265    0.002    0.000    0.004    0.000 C:\Users\OLab\AppData\Local\Continuum\anaconda3\lib\site-packages\pyomo\core\expr\numeric_expr.py:186(__getstate__)
        2    0.001    0.001    0.002    0.001 <frozen importlib._bootstrap_external>:914(get_data)
     1338    0.001    0.000    0.002    0.000 C:\Users\OLab\AppData\Local\Continuum\anaconda3\lib\site-packages\pyomo\core\expr\numvalue.py:545(__getstate__)

sys.getsizeof()得到的arguments的大小和第二台电脑上的.start()的profile

 - Data size: 56
 - Init_nodes size: 72
 - Queue size: 56
 - Shared incumbent data size: 56

         7257 function calls (7247 primitive calls) in 1.742 seconds

   Ordered by: internal time
   List reduced from 184 to 5 due to restriction <5>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        2    1.722    0.861    1.730    0.865 {method 'dump' of '_pickle.Pickler' objects}
        1    0.009    0.009    0.009    0.009 {built-in method _winapi.CreateProcess}
     1265    0.002    0.000    0.005    0.000 C:\Users\Palbo\Anaconda2\envs\py3\lib\site-packages\pyomo\core\expr\numeric_expr.py:186(__getstate__)
     1339    0.002    0.000    0.003    0.000 C:\Users\Palbo\Anaconda2\envs\py3\lib\site-packages\pyomo\core\expr\numvalue.py:545(__getstate__)
     1523    0.001    0.000    0.001    0.000 {built-in method builtins.hasattr}

干杯!

第一台计算机的规格应该更快但不是：

• Windows 10 Pro 适用于工作站
• Intel Xeon Silver 4114 CPU @2.20 GHz 2.19 GHz（每个 10 核）
• 64 GB 内存

第二台电脑规格：

• Windows 8.1
• Intel Core i3-2348M CPU @2.30 Ghz 2.30 Ghz（每个 2 核）
• 6 GB 内存

Answer 1

Finally found a solution by dumping the pickling of the arguments of the function into a file, then passing the name of the file as an argument for the worker() function, then opening each file from within the function in each parallel process.

转储时间从 ~24[s] 下降到 ~0.005[s]！

def worker(pickled_file_name, queue, shared_incumbent): 

    with open(pickled_file_name, "rb") as f:
        data_tuple = pickle.load(f, encoding='bytes')
    instance, data, optsolver, int_var_list, process_name, relaxed_incumbent = data_tuple
    return

def foo():
    [...]
    picklefile = open("pickled_vars"+str(i_nodo)+str(j_nodo)+".p", "wb") 
    picklefile.write(pickle.dumps(variables_,-1))
    picklefile.close()
                
    process_dict[process_name] = multiprocessing.Process(target=bnbparallelbranching, args=("pickled_vars"+str(i_nodo)+str(j_nodo)+".p", q, shared_incumbent_data))
    process_dict[process_name].start()