Although I often have embarrasingly parallel problems (data parallel), and I have an 8-CPU box at work, I used to frown on writing parallel computing code when doing exploratory coding. We now have fantastic parallel computing facilities in Python (amongst other, multiprocessing, IPython, and parallel Python). However, in my opinion, there are two reasons to hesitate to use them, especially when the code is very imature (which is almost always my case, in research settings):
- It makes the code look less like the ideas it is trying to express. Peter Norvig made a pretty convincing case for scientific code reading like math at SciPy2009.
- Because parallel computing is out of process, in Python, it is simply harder to debug (though I hear that the IPython guys are on that).
I have progressively developed a tiny tool to adress both problems, at least for my embarrasingly-parallel problems. I address the second problem by having a trivial switch to run my code without importing any fancy parallel computing tools. And I address the first problem using syntactic sugar to be able to type in map/reduce code that actually looks like standard procedural code:
results = Parallel(n_jobs=2)( delayed(my_calculation)(data1, data2, parameter1=1, parameter2=2) for data1 in store1 for data2 in store2)
There are several tricks here:
- I use a ‘delayed‘ decorator that creates the argument list and keyword argument dictionary for me so that I can type something that really looks like a function call. Also, the decorator checks to see if the function and the arguments can be pickled, because if not the parallel computing libraries will raise errors, sometimes with hard-to-understand messages.
- I use list comprehension to create the list to apply the map/reduce onto. List comprehension is really readable, and very powerful.
- The ‘Parallel‘ object hides all the cleverness. If the ‘n_jobs‘ parameter is set to 1, it does not call any parallel computing library. If it is set to -1, all the CPUs are used. The object instantiates the parallel computing context and also destroys it. While this is inefficient, it is great for catching errors early. And finally, while I have implemented this only for the multiprocessing module, any fork/join-based parallel computing library could be encapsulated the same way, thus providing a uniform API to do multi-node parallel computing or single-computer shared memory (as multi-processing uses the Unix fork call, and all modern Unices implement copy on write of memory pages, you get some shared memory for free without worrying about race conditions).
This pattern has actually evolved in the joblib project , which provides a lot of cleverness under the hood.