Running individual processes in parallel - Python

Question

I am using the python multiprocessing module to run a single process on multiple cores, but I want to run several independent processes in parallel. For ex - Process one analyzes a large file, processes two search patterns in different files, and process 3 performs some calculations; can all three different processed files that have different arguments be parallel?

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()

Process1 Process2 Process3 , . 2 + 2,5 + 2,25 = 6,75 . ? /, (Process2) , Final Process.

.

AK

Answer 1

16.6.1.5. :

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]"

, apply_async , .

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
# (...)