python 多进程并行密集型计算，单参数于多参数的实例

import multiprocessing
import time
import numba as nb

class parallelPool:

    def __init__(self,cpu_count:int=multiprocessing.cpu_count()):
        self.pool = multiprocessing.Pool(cpu_count)
        self.results = []
        self.taskList = []

    def submit(self,funcItem:tuple or list):
        # print(type(funcItem))
        if type(funcItem) == tuple:
            self._run(funcItem)
        elif type(funcItem) == list:
            for item in funcItem:
                self._run(item)

    def _run(self,item:tuple):
        arg = []
        for i in range(1, item.__len__()):
            arg.append(item[i])
        self.taskList.append(self.pool.apply_async(item[0], args=tuple(arg)))

    def execute(self):
        for res in self.taskList:
            self.results.append(res.get())
        # # 获取结果
        self.pool.close()
        self.pool.join()
        return self.results

@nb.jit()
def _fbnq(n: int):
    """实际计算函数"""
    if n < 1:
        return 1
    else:
        return _fbnq(n - 1) + _fbnq(n - 2)

def task1(n):
    return _fbnq(n)

def task2(n):
    return _fbnq(n)

def task3(n,p,g):
    return _fbnq(n) + p + g

if __name__ == '__main__':
    # 创建进程池（使用所有CPU核心）
    st = time.time()
    # 准备不同任务列表
    tasks = [
        (task1, 40), (task2, 40), (task3, 40,1,1),
        (task1, 40), (task2, 40), (task3, 40,1,1),
        (task1, 40), (task2, 40), (task3, 40, 1,3),
        (task1, 40), (task2, 40), (task3, 40, 1,4),
        (task1, 40), (task2, 40), (task3, 40, 1,5),
        (task1, 40), (task2, 40), (task3, 40, 1,6)
    ]*3

    # 并行执行任务

    pl = parallelPool()
    # for item in tasks: # 9.326000213623047  9.46895694732666  23.66971516609192
    #     pl.submit(item)
    pl.submit((task3,41,1,1))
    pl.submit(tasks)   # 9.486266851425171  9.4131498336792  23.88749670982361
    rlts = pl.execute()

    print(rlts)
    print(rlts.__len__())
    print(time.time()-st)