python - 并行运行单独的进程 - Python

Question

我使用 python 的“多处理”模块在多个内核上运行单个进程，但我想并行运行几个独立的进程。

例如，进程一解析大文件，进程二在不同文件中寻找模式，进程三做一些计算；所有这三个具有不同参数集的不同进程都可以并行运行吗？

def Process1(largefile):
    Parse large file
    runtime 2hrs
    return parsed_file

def Process2(bigfile)
    Find pattern in big file
    runtime 2.5 hrs
    return pattern

def Process3(integer)
    Do astronomical calculation
    Run time 2.25 hrs
    return calculation_results

def FinalProcess(parsed,pattern,calc_results):
    Do analysis
    Runtime 10 min
    return final_results

def main():
parsed = Process1(largefile)
pattern = Process2(bigfile)
calc_res = Process3(integer)
Final = FinalProcess(parsed,pattern,calc_res)

if __name__ == __main__:
    main()
    sys.exit()

在上面的伪代码中，Process1、Process2 和 Process3 是单核进程，即它们不能在多个处理器上运行。这些过程按顺序运行，耗时 2+2.5+2.25hrs = 6.75 hrs。是否可以并行运行这三个进程？这样它们就可以同时在不同的处理器/内核上运行，并且当大部分时间(Process2)完成时我们会转到最终过程。

Answer 1

来自 16.6.1.5. Using a pool of workers :

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    pool = Pool(processes=4)              # start 4 worker processes
    result = pool.apply_async(f, [10])    # evaluate "f(10)" asynchronously
    print result.get(timeout=1)           # prints "100" unless your computer is *very* slow
    print pool.map(f, range(10))          # prints "[0, 1, 4,..., 81]"

因此，您可以对池应用异步并在一切准备就绪后获取结果。

from multiprocessing import Pool

# all your methods declarations above go here
# (...)

def main():
    pool = Pool(processes=3)
    parsed = pool.apply_async(Process1, [largefile])
    pattern = pool.apply_async(Process2, [bigfile])
    calc_res = pool.apply_async(Process3, [integer])

    pool.close()
    pool.join()

    final = FinalProcess(parsed.get(), pattern.get(), calc_res.get())

# your __main__ handler goes here
# (...)