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Leon
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Why does Matlab require a Parallel computing toolbox in order to use more CPU cores?

Asked by Leon
on 13 Jun 2019
Latest activity Commented on by Steven Lord
on 14 Jun 2019
Is it true that if I have a multi-core CPU computer and Matlab will only use one of the CPU cores, unless I leverage the parallel computing toolbox and use "parfor" instead of "for"?
Shouldn't it be natural that Matlab automatically decide when to utlize more of my CPU cores depending on the need?
Weird enough, sometimes parfor actually slows down my processing.

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1 Answer

Answer by Walter Roberson
on 13 Jun 2019

Not quite. Matlab recognizes some patterns of operations and for sufficiently large matrices automatically calls high performance multicore libraries that use knowledge of cache behaviour and vectorized hardware instructions to improve performance. These execute within one process.
Running larger sections independently requires starting multiple processes and transferring the data back and forth and analyzing what really needs to be sent to each process. The parallel computing toolbox does this work.
Because of the process overhead and the data transfer overhead and the loss of opportunity to take advantage of multiple CPU, it is very common for parfor to come out slower.

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Very good analogy indeed! It helps me understand why it is actually slower by using parfor.
Here is the problem. If it is about shopping with my kids, I can make that decision easily. For my Matlab processing, sometimes I can make that decision easily as well. The problem is that a lot of the other times I'm in a dark box in terms of deciding when to use parfor and when to use for. That's why I think some sort of algorithm will help.
As a quick test: monitor CPU usage while you operate with a "for". If it averages 1 to 2 cpus, and not so much memory, then if you can manage to find quite independent subsections of the task, parfor might well be better. But if you see significant bursts where all of the physical CPUs are in use, then it becomes doubtful that parfor could improve anything.
If it involves 1 to 2 cpus and a lot of memory, then whether parfor would help or not depends a lot on the memory access pattern. If it can be broken down into local access (say a row or column at a time) then parfor might be fine. If an entire array is used, such as large matrix multiply kind of data pattern, then parfor will probably need to transfer too much memory to benefit much.
You know more about your problem than MATLAB does, so it would likely be more difficult for MATLAB to "know" if parfor is going to be beneficial (beneficial meaning the increase in time due to the parallel overhead would be less than the decrease in time from running multiple iterations simultaneously in parallel.)
Running your code on smallers subsets of your data set would not only allow you to validate that the code is working correctly, it would also allow you to investigate the performance profile of your problem. If you want to ramp up to say a million data points, start off with a hundred and compare parfor and for. Repeat for a couple of larger subsets (say a thousand points and ten thousand points.)
Extrapolating from a few measurements can be dangerous but you're not trying to determine "Running this code for a million points will take exactly X seconds", you're trying to determine a trend. At the very least you may get a rough sense of whether or not parfor will obviously be beneficial to runtime, will obviously be detrimental to runtime, or whether it's unclear.
To extend the shopping analogy a bit closer to its breaking point, you take your kids to the grocery store on a Thursday morning in April when the store isn't busy and ask them to help by getting a subset of your list when you have only a couple items. You do this a couple times, with increasingly long lists, to determine if it'll be worth asking them for help when you're going shopping the day before Thanksgiving when the store is wall-to-wall shopping carts and you're getting approximately a hundred pounds of food for a big family dinner.

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