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.
In : import affinity In : affinity.get_process_affinity_mask(0) Out: 63
This is good: 63 is a bitmask corresponding to 111111 – meaning all 6 cores are available to Python. Now running this, I get:
In : import numpy as np In : affinity.get_process_affinity_mask(0) Out: 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:
import numpy as np import affinity import multiprocessing affinity.set_process_affinity_mask(0,2**multiprocessing.cpu_count()-1)