python 如果有導入numpy模塊的import語句，會導致默認將多進程程序的每個進程都綁定到同一個CPU core上，
失去了多進程在多核CPU上的性能優越性，這和CPU affinity(CPU親和性)有關，解決辦法：
導入affinity包，執行：
affinity.set_process_affinity_mask(0,2**multiprocessing.cpu_count()-1)
以下是英文文檔原文，供參考：
Python refuses to use multiple cores – solution
I was trying to get parallel Python to work and I noticed that if I run two Python scripts simultaneously – say, in two different terminals – they use the same core. Hence, I get no speedup from multiprocessing/parallel Python. After some searching around, I found out that in some circumstances importing numpy causes Python to stick all computations in one core. This is an issue with CPU affinity, and apparently it only happens for some mixtures of Numpy and BLAS libraries – other packages may cause the CPU affinity issue as well.
There’s a package called affinity (Linux only AFAIK) that lets you set and get CPU affinity. Download it, run python setup.py install, and run this in Python or ipython:
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In [
1
]:
import
affinity
In [
2
]: affinity.get_process_affinity_mask(
0
)
Out[
2
]:
63
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This is good: 63 is a bitmask corresponding to 111111 – meaning all 6 cores are available to Python. Now running this, I get:
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In [
4
]:
import
numpy as np
In [
5
]: affinity.get_process_affinity_mask(
0
)
Out[
5
]:
1
|
So now only one core is available to Python. The solution is simply to set the CPU affinity appropriately after import numpy, for instance:
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import
numpy as np
import
affinity
import
multiprocessing
affinity.set_process_affinity_mask(
0
,
2
*
*
multiprocessing.cpu_count()
-
1
)
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