使用Python通过多处理在各种处理器上执行程序

Question

我想同时在不同的处理器上运行不同的可执行文件，所以我从这里举了例子： 有没有办法使用Python将不同的作业（进程）分配给Linux中的特定核心？ 并尝试运行可执行文件，但是 FOR 循环等待可执行文件完成以分配新进程。有没有办法将新进程分配给下一个核心而无需等待？

import os
from itertools import cycle
import multiprocessing as mp
import subprocess

def map_hack(AFF):
  my_pid = os.getpid()
  old_aff = os.sched_getaffinity(0)
  os.sched_setaffinity(0,AFF)
  return (my_pid, old_aff, os.sched_getaffinity(0))

PROCESSES = os.cpu_count()
_mycpus = cycle(os.sched_getaffinity(0))
cpus = [[next(_mycpus)] for x in range(PROCESSES)]




with mp.Pool(processes=PROCESSES) as pool:
  for x in pool.map(map_hack, cpus, chunksize=1):
     print("Try 2:My pid is {} and my old aff was {}, my new aff is {}".format(*x))
    z = str( x[2])
    z = z[:-1]
    z = int( z[-1:])
    print (z)
    if (z == 0):
        print("program waits for this executable to finish")
        subprocess.run(["./exevision"])

pool.close()
pool.join()

Answer 1

试试这个：

import os
from itertools import cycle
import multiprocessing as mp
import subprocess

def map_hack(AFF):
    my_pid= os.getpid()
    old_aff = os.sched_getaffinity(0)
    os.sched_setaffinity(0, AFF)
    print("My pid is {} and my old aff was {}, my new aff is {}".format(
        my_pid, old_aff,os.sched_getaffinity(0)))
    subprocess.run(["./exevision"])
    return (my_pid,old_aff, os.sched_getaffinity(0))

PROCESSES = os.cpu_count()
_mycpus= cycle(os.sched_getaffinity(0))
cpus = [[next(_mycpus)] for _ in range(PROCESSES)]

with mp.Pool(processes=PROCESSES) as pool:
    results =[pool.apply_async(map_hack, args=(affinity,)) for affinity in cpus]
    for res in results:
        res.get()

pool.close()
pool.join()