问题：为什么在导入numpy之后多处理仅使用单个内核？
我不确定这是否更多的是操作系统问题，但是我想在这里问一下，以防有人对Python有所了解。
我一直在尝试使用并行化CPU繁重的for循环joblib，但是我发现不是将每个工作进程分配给不同的内核，而是最终将所有工作进程分配给相同的内核，并且没有性能提升。
这是一个非常简单的例子…
from joblib import Parallel,delayed
import numpy as np
def testfunc(data):
# some very boneheaded CPU work
for nn in xrange(1000):
for ii in data[0,:]:
for jj in data[1,:]:
ii*jj
def run(niter=10):
data = (np.random.randn(2,100) for ii in xrange(niter))
pool = Parallel(n_jobs=-1,verbose=1,pre_dispatch='all')
results = pool(delayed(testfunc)(dd) for dd in data)
if __name__ == '__main__':
run()
… htop这是该脚本运行时看到的内容：
我在具有4核的笔记本电脑上运行Ubuntu 12.10（3.5.0-26）。显然joblib.Parallel是为不同的工作人员生成了单独的进程，但是有什么方法可以使这些进程在不同的内核上执行？
I am not sure whether this counts more as an OS issue, but I thought I would ask here in case anyone has some insight from the Python end of things.
I’ve been trying to parallelise a CPU-heavy for loop using joblib, but I find that instead of each worker process being assigned to a different core, I end up with all of them being assigned to the same core and no performance gain.
Here’s a very trivial example…
from joblib import Parallel,delayed
import numpy as np
def testfunc(data):
# some very boneheaded CPU work
for nn in xrange(1000):
for ii in data[0,:]:
for jj in data[1,:]:
ii*jj
def run(niter=10):
data = (np.random.randn(2,100) for ii in xrange(niter))
pool = Parallel(n_jobs=-1,verbose=1,pre_dispatch='all')
results = pool(delayed(testfunc)(dd) for dd in data)
if __name__ == '__main__':
run()
…and here’s what I see in htop while this script is running:
I’m running Ubuntu 12.10 (3.5.0-26) on a laptop with 4 cores. Clearly joblib.Parallel is spawning separate processes for the different workers, but is there any way that I can make these processes execute on different cores?
回答 0
经过更多的谷歌搜索后，我在这里找到了答案。
事实证明，某些Python模块（numpy，scipy，tables，pandas，skimage对进口核心相关性……）的混乱。据我所知，这个问题似乎是由它们链接到多线程OpenBLAS库引起的。
解决方法是使用
os.system("taskset -p 0xff %d" % os.getpid())在导入模块之后粘贴了这一行，我的示例现在可以在所有内核上运行：
到目前为止，我的经验是，这似乎对numpy机器的性能没有任何负面影响，尽管这可能是特定于机器和任务的。
更新：
还有两种方法可以禁用OpenBLAS本身的CPU关联性重置行为。在运行时，您可以使用环境变量OPENBLAS_MAIN_FREE（或GOTOBLAS_MAIN_FREE），例如
OPENBLAS_MAIN_FREE=1 python myscript.py或者，如果您要从源代码编译OpenBLAS，则可以在构建时通过编辑Makefile.rule使其包含该行来永久禁用它
NO_AFFINITY=1After some more googling I found the answer here.
It turns out that certain Python modules (numpy, scipy, tables, pandas, skimage…) mess with core affinity on import. As far as I can tell, this problem seems to be specifically caused by them linking against multithreaded OpenBLAS libraries.
A workaround is to reset the task affinity using
os.system("taskset -p 0xff %d" % os.getpid())
With this line pasted in after the module imports, my example now runs on all cores:
My experience so far has been that this doesn’t seem to have any negative effect on numpy‘s performance, although this is probably machine- and task-specific .
Update:
There are also two ways to disable the CPU affinity-resetting behaviour of OpenBLAS itself. At run-time you can use the environment variable OPENBLAS_MAIN_FREE (or GOTOBLAS_MAIN_FREE), for example
OPENBLAS_MAIN_FREE=1 python myscript.py
Or alternatively, if you’re compiling OpenBLAS from source you can permanently disable it at build-time by editing the Makefile.rule to contain the line
NO_AFFINITY=1
回答 1
Python 3现在公开了直接设置亲和力的方法
>>> import os
>>> os.sched_getaffinity(0)
{0, 1, 2, 3}
>>> os.sched_setaffinity(0, {1, 3})
>>> os.sched_getaffinity(0)
{1, 3}
>>> x = {i for i in range(10)}
>>> x
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
>>> os.sched_setaffinity(0, x)
>>> os.sched_getaffinity(0)
{0, 1, 2, 3}
回答 2
这似乎是Ubuntu上Python的常见问题，并不特定于joblib：
我建议尝试使用CPU相似性（taskset）。
