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Re: Advice regarding multiprocessing module

Abhinav M Kulkarni
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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))
# Blocking wait for each process to finish
for p in jobs:
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.


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.
> Cheers,
> JM
> The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.

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