Re: Advice regarding multiprocessing module
On 11 March 2013 14:57, Abhinav M Kulkarni wrote: > 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)) Use a multiprocessing.Pool for this, rather than creating one process for each job. e.g.: p = Pool(4) # 1 process for each core results = [] for ib, fb in zip(intermediateBoards, finalizedBoards): results.append(p.apply_async(TrainGoCRFIsingGibbs, args=(ib, fb, param, q))) p.close() p.join() # To retrieve the return values for r in results: print(r.get()) This will distribute your jobs over a fixed number of processes. You avoid the overhead of creating and killing processes and the process switching that occurs when you have more processes than cores. > 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. Do this without using multiprocessing first. Loosely you can hope that multiprocessing would give you a factor of 4 speedup but no more. You haven't reported a comparison of times with/without multiprocessing so it's not clear that that is the issue. Oscar -- http://mail.python.org/mailman/listinfo/python-list
Re: Advice regarding multiprocessing module
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 -- IMPORTANT NOTICE: 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. -- http://mail.python.org/mailman/listinfo/python-list
Re: Advice regarding multiprocessing module
On 03/11/2013 01:57 AM, Abhinav M Kulkarni wrote: * 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). * 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). Seems you only need 4 processes, as you have 4 cores. Instead of creating a new one each time, reuse the same 4 processes, letting each do a quarter of the data. It's not the process creation that's particularly slow, but all the initialization of starting another instance of Python. If you're on Linux, you might be able to speed that up by using fork, but I don't specifically know. -- DaveA -- http://mail.python.org/mailman/listinfo/python-list
Re: Advice regarding multiprocessing module
- 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 -- IMPORTANT NOTICE: 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. -- http://mail.python.org/mailman/listinfo/python-list
Advice regarding multiprocessing module
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 -- http://mail.python.org/mailman/listinfo/python-list
