Advice regarding multiprocessing module

[Abhinav M Kulkarni]

Hi Jean,

Below is the code where I am creating multiple processes:

if __name__ == '__main__':
     # List all files in the games directory
     files = list_sgf_files()

     # Read board configurations
     (intermediateBoards, finalizedBoards) = read_boards(files)

     # Initialize parameters
     param = Param()

     # Run maxItr iterations of gradient descent
     for itr in range(maxItr):
         # Each process analyzes one single data point
         # They dump their gradient calculations in queue q
         # Queue in Python is process safe
         start_time = time.time()
         q = Queue()
         jobs = []
         # Create a process for each game board
         for i in range(len(files)):
             p = Process(target=TrainGoCRFIsingGibbs, 
args=(intermediateBoards[i], finalizedBoards[i], param, q))
             p.start()
             jobs.append(p)
         # Blocking wait for each process to finish
         for p in jobs:
             p.join()
         elapsed_time = time.time() - start_time
         print 'Iteration: ', itr, '\tElapsed time: ', elapsed_time

As you recommended, I'll use the profiler to see which part of the code 
is slow.

Thanks,
Abhinav

On 03/11/2013 04:14 AM, Jean-Michel Pichavant wrote:
> ----- Original Message -----
>
>> 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
> Hi,
>
> Can you post some code, especially the part where you're create/running the processes ? If it's not too big, the process function as well.
>
> Either multiprocess is slow like you stated, or you did something wrong.
>
> Alternatively, if posting code is an issue, you can profile your python code, it's very easy and effective at finding which the code is slowing down everyone.
> http://docs.python.org/2/library/profile.html
>
> Cheers,
>
> JM
>
>
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