Python Multithreading common Package comparison

Python cannot achieve true multicore parallelism because of its own nature, but there are some third-party libraries that better simulate parallel operations in multi-core environments, such as PP packets and multiprocessing, so which can make the most of multi-core?

Here I make a simple contrast, first put the conclusion: multiprocessing is the best.

In the actual measurement process, my CPU is 4 core 8 threads, multiprocessing can take full advantage of the multi-core computing advantages, so that each CPU core load is basically equivalent, and can be loaded on 8 process cores, the overall CPU performance, and PP bag can only use 4 physical core, Basic disregard for the remaining 4 virtual cores.

The following sample code is for everyone to run the reference:

Import Multiprocessingimport Timeimport ppdef func (n):    sum = 0 for    i in Xrange (n):        sum + = I    return sumif _ _name__ = = "__main__":    multiprocessing.freeze_support ()    start = Time.clock () for    I in Xrange (200000):        sum = func (10000)    print ">> normal:", Time.clock ()-start    start = Time.clock ()    pool = Multiprocessing. Pool (processes=8)    jobs = [] for    I in Xrange (200000):        jobs.append (Pool.apply_async (func, (10000,)))    pool.close ()    pool.join ()    print ">> multiprocessing:", Time.clock ()-Start    start = Time.clock ()    jobs = []    job_server = pp. Server ()    Job_server.set_ncpus (8) for    I in Xrange (200000):        jobs.append (Job_server.submit (func, ( 10000,))    job_server.wait ()    print ">> pp:", Time.clock ()-Start    job_server.print_stats ()

　　

Python Multithreading common Package comparison