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OCTAVE FOR LOOP CODE
By using these techniques, we've seen users speed up their code over 32x while still using a single machine.Necessary cookies are absolutely essential for the website to function properly. Modern statistical languages make it incredibly easy to parallelize your code across cores, and Domino makes it trivial to access very powerful machines, with many cores. Note that you can use the parcellfun function if your inputs are not numbers (e.g., if they are file names or product identifiers).
OCTAVE FOR LOOP INSTALL
% you'll need to run this once, to install the package: Here's how you can use it: if exist('OCTAVE_VERSION') ~= 0 Unfortunately, Octave doesn't have a nice parfor equivalent - but it does have its own Parallel package. Note that if your inputs are not integers (e.g., they are file names or item identifiers), you can use the parcellfun function, which operates on cell inputs, rather than array inputs. % assumes that processInput is defined in a separate function file Matlab's Parallel Computing Toolbox makes it trivial to use parallel for loops using the parfor construct. You can find some more info on the difference between mclapply and parLapply on this StackOverflow postĪs an alternative, you can also use the foreach package, which lets you use a familiar for loop syntax, automatically parallelizing your code under the hood: library(foreach) Results = parLapply(cl, inputs, processInput) # the above won't work on Windows, but this will: Results = mclapply(inputs, processInput, mc.cores = numCores) Since 2.14, R has included the Parallel library, which makes this sort of task very easy.
Results = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs # what are your inputs, and what operation do you want to Python has a great package, that makes parallelism incredibly easy. You would use your specific data and logic, of course. To make our examples below concrete, we use a list of numbers, and a function that squares the numbers.
OCTAVE FOR LOOP HOW TO
Instead of processing your items in a normal a loop, we'll show you how to process all your items in parallel, spreading the work across multiple cores.
Normally you would loop over your items, processing each one: for i in inputs (After this step, you can then combine your results however you want, e.g., aggregating them, saving them to a file - it doesn't matter for our purposes.)
Perf stats from some parallelized Python code running on a single, 32-core machine Is my Code Parallelizable?įor the purpose of this post, we assume a common analysis scenario: you need to perform some calculation on many items, and the calculation for one item does not depend on any other.
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