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POWhat's the best way to divide large files in Python for multiprocessing?
 

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  1. POWhat's the best way to divide large files in Python for multiprocessing?
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    <p>I run across a lot of "embarrassingly parallel" projects I'd like to parallelize with the <code>multiprocessing</code> module. However, they often involve reading in huge files (greater than 2gb), processing them line by line, running basic calculations, and then writing results. What's the best way to split a file and process it using Python's multiprocessing module? Should <code>Queue</code> or <code>JoinableQueue</code> in <code>multiprocessing</code> be used? Or the <code>Queue</code> module itself? Or, should I map the file iterable over a pool of processes using <code>multiprocessing</code>? I've experimented with these approaches but the overhead is immense in distribution the data line by line. I've settled on a lightweight pipe-filters design by using <code>cat file | process1 --out-file out1 --num-processes 2 | process2 --out-file out2</code>, which passes a certain percentage of the first process's input directly to the second input (see <a href="https://stackoverflow.com/questions/659865/python-multiprocessing-sharing-a-large-read-only-object-between-processes/659888#659888">this post</a>), but I'd like to have a solution contained entirely in Python. </p> <p>Surprisingly, the Python documentation doesn't suggest a canonical way of doing this (despite a lengthy section on programming guidelines in the <code>multiprocessing</code> documentation).</p> <p>Thanks, Vince</p> <p>Additional information: Processing time per line varies. Some problems are fast and barely not I/O bound, some are CPU-bound. The CPU bound, non-dependent tasks will gain the post from parallelization, such that even inefficient ways of assigning data to a processing function would still be beneficial in terms of wall clock time.</p> <p>A prime example is a script that extracts fields from lines, checks for a variety of bitwise flags, and writes lines with certain flags to a new file in an entirely new format. This seems like an I/O bound problem, but when I ran it with my cheap concurrent version with pipes, it was about 20% faster. When I run it with pool and map, or queue in <code>multiprocessing</code> it is always over 100% slower. </p>
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    1. COThis is a big gripe of mine about an otherwise dandy scripting language --simple concurrent computation is a pain without threads. Sure, you can get it done, but some jobs are just a lot simpler with a thread-and-lock model.
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    2. COA threaded "parallel" version will (I believe) never be faster, except for the fact that threads are faster to create than processes. The GIL is a huge bottleneck for CPU-bound multithreaded programs. Also, there are no mutable objects that need to be shared between processes/threads, so multithreading is not really needed over multiprocessing.
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    3. CO@Vince actually, it will all depend on the exact circumstance. In yours, it might never be. In others, it might. My point is that for most of the concurrent operations I've needed to do (in C), there has rarely been a justification for using the extra needed for proper IPC when threads and locks give a much simpler model. For larger problems that need to scale better and across different machines, it's a different story.
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