Dear all,
I need some advice regarding use of the multiprocessing module.
Following is the scenario:
* I am running gradient descent to estimate parameters of a pairwise
grid CRF (or a grid based graphical model). There are 106 data
points. Each data point can be analyzed in parallel.
* To calculate gradient for each data point, I need to perform
approximate inference since this is a loopy model. I am using Gibbs
sampling.
* My grid is 9x9 so there are 81 variables that I am sampling in one
sweep of Gibbs sampling. I perform 1000 iterations of Gibbs sampling.
* My laptop has quad-core Intel i5 processor, so I thought using
multiprocessing module I can parallelize my code (basically
calculate gradient in parallel on multiple cores simultaneously).
* I did not use the multi-threading library because of GIL issues, GIL
does not allow multiple threads to run at a time.
* As a result I end up creating a process for each data point (instead
of a thread that I would ideally like to do, so as to avoid process
creation overhead).
* I am using basic NumPy array functionalities.
Previously I was running this code in MATLAB. It runs quite faster, one
iteration of gradient descent takes around 14 sec in MATLAB using parfor
loop (parallel loop - data points is analyzed within parallel loop).
However same program takes almost 215 sec in Python.
I am quite amazed at the slowness of multiprocessing module. Is this
because of process creation overhead for each data point?
Please keep my email in the replies as I am not a member of this mailing
list.
Thanks,
Abhinav
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