我有两个过程，一个创建共享内存，第二个访问共享内存。我正在使用Python 3.8。
第二个进程可以选择仅使用python函数或通过cython访问共享内存。 cython选项失败-我得到了SIGSEGV。
是否有一些特殊的方法可以在cython中获取共享内存？ docs似乎没有显示如何实际获取python共享内存。
进程1（setup_shm.py）：
from multiprocessing import shared_memory
import numpy as np
import argparse
from package.get_shm import main as get_shm
from multiprocessing import Process
def main(cython=None):
arr_share = np.array([[1.3424,23.24324],[1.4234,.08682]],dtype='f')
shape,dtype = arr_share.shape,arr_share.dtype
# Create a shared memory of size arr_share.nbytes
shm = shared_memory.SharedMemory(create=True,size=arr_share.nbytes)
# Create array using the buffer of shm
shm_np_array = np.ndarray(shape=shape,dtype=dtype,buffer=shm.buf)
# Copy the data into the shared memory
np.copyto(shm_np_array,arr_share)
print('shared name {}'.format(shm.name))
print('shared size {}'.format(arr_share.nbytes))
print(arr_share)
p = Process(target=get_shm,args=(shm.name,),kwargs={'cython': cython})
p.start()
p.join()
shm.close()
shm.unlink()
def setup():
parser = argparse.ArgumentParser(description='Shared mem getter')
parser.add_argument('--cython',action='store_true',help='Use cython to get shared mem')
args = parser.parse_args()
main(cython=args.cython)
if __name__ == "__main__":
setup()
进程2（get_shm.py）：
from multiprocessing.shared_memory import SharedMemory
import numpy as np
import argparse
import sys
from c_get_shm import Vectors
def main(name,cython=None):
val = None
if cython:
print('getting vector using cython - {}'.format(name))
vectors = Vectors(name)
try:
val = vectors.data.base[0]
except Exception as e:
raise (e)
else:
shm = SharedMemory(name=name)
np_array = np.ndarray(shape=(2,2),dtype='f',buffer=shm.buf)
print('getting vector using python - {}'.format(name))
val = np_array[0]
shm.close()
print('val is {}'.format(val))
sys.stdout.flush()
return
def setup():
parser = argparse.ArgumentParser(description='Shared mem getter')
parser.add_argument('name',type=str,help='Shared memory name')
parser.add_argument('--cython',help='Use cython to get shared mem')
args = parser.parse_args()
main(name=args.name,cython=args.cython)
if __name__ == "__main__":
setup()
Cython类（c_get_shm.pyx）
#!python
#cython: language_level=3,boundscheck=False
from multiprocessing import shared_memory
cimport numpy as np
np.import_array()
cdef class Vectors:
cdef public object shm_name
cdef public float[:,:] data
def __init__(self,shm_name):
print('Vectors Class for getting shared memory - {}'.format(shm_name))
self.__get_shared_array(shm_name)
print('data is {},{}'.format(self.data,self.data.shape))
def __get_shared_array(self,shm_name: str):
print('get_shared_array {}'.format(shm_name))
shm = shared_memory.SharedMemory(name=shm_name)
print('shm buf is {} size {}'.format(shm.buf,shm.buf.nbytes))
self.data = np.ndarray(shape=(2,buffer=shm.buf)
Setup.py
from setuptools import setup,Extension
from Cython.Build import cythonize
extensions = [
Extension("c_get_shm",["./c_get_shm.pyx"],libraries=['rt'])
]
setup(
ext_modules=cythonize(extensions,gdb_debug=True)
)
我正在使用任一脚本运行
- python ./setup_shm.py --cython
- python ./setup_shm.py
我设法看到SIGSEGV的唯一方法是使用strace：
Vectors Class for getting shared memory - psm_23cd04df
get_shared_array psm_23cd04df
shm buf is <memory at 0x7f8b4d66df40> size 16
data is <MemoryView of 'ndarray' object>,[2,2,0]
[{WIFSIGNALED(s) && WTERMSIG(s) == SIGSEGV && WCOREDUMP(s)}],NULL) = 868
--- SIGCHLD {si_signo=SIGCHLD,si_code=CLD_DUMPED,si_pid=868,si_uid=1000,si_status=SIGSEGV,si_utime=0,si_stime=0} ---
close(5) = 0
munmap(0x7f8b65698000,16) = 0
close(4) = 0
unlink("/dev/shm/psm_23cd04df") = 0
我尝试了gdb python并从那里运行脚本，但是没有堆栈跟踪。