POSIX multiprocessing spawn performance becomes 10x slower from a certain pickle size
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Evaluación
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Aptitud para principiantes
- 38/100
- Tipo de issue
- Error
- Claridad
- Bastante claro
- Estado de actividad
- Estancado
- Stack tecnológico
- linux, python
- Área
- operating-systems, performance
Línea de trabajo
Reproduce la diferencia de tiempos con mp_pipe_limits.py en Python 3.9 o 3.10 y, después, inspecciona multiprocessing/popen_spawn_posix.py alrededor de la creación de pipes y reduction.dump. Compara el comportamiento con 65536 y 65537 bytes y determina una solución que evite el salto brusco de rendimiento; se considera terminado cuando la reproducción ya no muestra la ralentización indicada sin depender de un tamaño de pipe fijo e inseguro.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
Bug report
We are using multiprocessing with the spawn start method. On my 32-thread PC, starting all worker processes for my project used to take 2 seconds. At a certain point, it jumped straight to taking 20 seconds.
The slowdown appears as soon as more than 64 KB needs to be sent to a child process over the pipe.
Consider this minimal reproduction case:
#!/usr/bin/python3
import multiprocessing
import random
import sys
import time
class Container:
def __init__(self, size):
self.data = random.randbytes(size)
class ChildProcess(multiprocessing.Process):
def __init__(self, name: str, container):
super().__init__(name=name)
self.container = container
def run(self) -> None:
print("Running")
def run():
fixed_overhead_3_9 = 885
difference = int(sys.argv[1])
container = Container((64*1024) - fixed_overhead_3_9 + difference)
children = [ChildProcess(f"child-{i}", container) for i in range(0, 2)]
start_time = time.perf_counter()
for child in children:
child.start()
end_time = time.perf_counter()
print(f"Running took {int((end_time - start_time) * 1000)}ms")
for child in children:
child.join()
if __name__ == "__main__":
multiprocessing.set_start_method("spawn")
run()
I added some "instrumentation" in multiprocessing/popen_spawn_posix.py to print the buffer size:
try:
reduction.dump(prep_data, fp)
reduction.dump(process_obj, fp)
finally:
set_spawning_popen(None)
print(len(fp.getbuffer()))
parent_r = child_w = child_r = parent_w = None
Running the example results in:
$ python3.9 mp_pipe_limits.py 0
65536
65536
Running took 9ms
Running
Running
$ python3.9 mp_pipe_limits.py 1
65537
65537
Running
Running took 96ms
Running
Changing the pipe size with fcntl in multiprocessing/popen_spawn_posix.py restores performance:
parent_r = child_w = child_r = parent_w = None
try:
parent_r, child_w = os.pipe()
child_r, parent_w = os.pipe()
fcntl.fcntl(parent_w, 1031, 100000)
cmd = spawn.get_command_line(tracker_fd=tracker_fd,
pipe_handle=child_r)
Where 1031 is fcntl.F_SETPIPE_SZ, which is not in Python 3.9.
Rerunning the reproduction case after this change:
$ python3.9 mp_pipe_limits.py 1
65537
65537
Running took 9ms
Running
Running
Of course, changing the pipe size will only delay the onset of the problem. The real solution (if there is any) will probably be different. Blindly setting a pipe size might also not be safe as it depends on limits set in /proc.
The example above is a best case example, since it has very limited pickle overhead. We hit this limit without any data caches involved. It's just our Python objects that live after application initialization. They are slower to pickle. However, then things are still 10x slower, so not just a fixed 80ms as seen in the example.
We use spawn instead of fork on Linux to avoid troubles with objects that cannot be pickled on other OSs (Windows).
Your environment
- CPython versions tested on: 3.9 and 3.10
- Operating system and architecture: Arch Linux (kernel 5.19.9-arch1-1), x86_64
- Lenguaje dominante
- Python
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