I have a program that call a m-file that contains parfor for calculation. You know that in MATLAB R2014a we don't need open parallel computing using parpool or something likes that and parfor doing the same.
My question is about closing parallel computing. If i have this structure ( only parfor ) MATLAB closing parallel computing after ending process of parfor? I'm calling this parfor every 10 seconds. I don't want MATLAB close the pool in every iteration of my system.
Thanks.
From the documentation of parpool:
If you set your parallel preferences to automatically create a
parallel pool when necessary, you do not need to explicitly call the
parpool command. You might explicitly create a pool to control when
you incur the overhead time of setting it up, so the pool is ready for
subsequent parallel language constructs.
It is true that we don't have to use parpool, but it makes sense to use it if you want to control the overhead it causes.
As for your question - take a look at the Parallel Computing Toolbox Preferences:
I believe that the highlighted option is what was bothering you. If the default timeout is too short, you could either postpone it or disable it altogether.
Related
I have written a code by matlab and i've used parallel computing toolbox
more description about my code:
i'm trying to implement parallel genetic algorithm by matlab and parallel computing toolbox.
i've implemented that but i've a problem. that is my parallel code with parfor is so much slower that serial one with for.
my code:
tic
for j=1:maxIteration
parfor i=1:numIslands
if migrationInterval
doMigration;
end
doCrossover;
doMutation;
newSpring;
end
end
toc
numIslands is always small number (5 to 12)
maxiteration is always big number (1500 to 5000)
please help me
thank you
I recommend you to run your function using "Run and Time" tool. The results will show, if the reason is in parfor-procedure or in your function.
It can be the parfor procedure is unnecesary and gives no advantage, but it depends always on the function you run.
You mention that your CPU has two cores. One problem might being the code itself, it looks as if you are calling scripts instead of functions, so you might be flooding your workspace unnecessarily by doing so. Furthermore, if any of those scripts is declaring variables on the fly, you might be clogging your RAM (Matlab is particularly good at that) thus making your code run slower.
Try to optimize each of the scripts first.
I would really recommend you to use functions instead of scripts though.
When I have the parallel computing toolbox installed and use parfor in my code, MATLAB starts the pool automatically once it reaches the parfor loop. This however makes it difficult to debug at times, which is why I would like to prevent MATLAB from opening a pool in certain situations. So, how can I tell MATLAB not to open a pool? Obviously I could go through my code and remove all parfor loops and replace them with normal for loops, but this is tedious and I might forget to undo my changes.
edit: To specify, I ideally would like the parfor loop to behave exactly like a for when setting a control or variable or something. That is, I should for example also be able to place breakpoints in the for-loop.
Under Home->parallel->parallel preferences you can deselect the check box "Automatically create a parallel pool (if one doesn't already exist) when parallel keywords are executed." This makes all the parfor loops behave as a normal for loop.
I'll get back to you if I figure out a way to do this in the code as opposed to using the check box.
Update turns out it is indeed possible to change the settings through code, although I would not recommend this, as it involves changing MATLAB's preference file. This is taken from the Undocumented MATLAB blog by Yair Altman.
ps = parallel.Settings;
ps.Pool
ans =
PoolSettings with properties:
AutoCreate: 1
RestartOnClusterChange: 1
RestartOnPreferredNumWorkersChange: 1
IdleTimeout: 30
PreferredNumWorkers: 12
where you need to change the AutoCreate switch to 0.
As alternative I'd suggest wrapping everything inside your parfor in a function, thus calling
parfor 1:N
output = function(..)
end
Now modify your script/function to have a Parallel switch on top:
if Parallel
parfor 1:N
output = function(..)
end
else
for 1:N
output = function(..)
end
end
You can edit and debug the function itself and set your switch on top of your program to execute in parallel or serial.
As well as the normal syntax
parfor i = 1:10
you can also use
parfor (i = 1:10, N)
where N is the maximum number of workers to be used in the loop. N can be a variable set by other parts of the code, so you can effectively turn on and off parallelism by setting the variable N to 1 or 0.
Edit: to be clear, this only controls the number of workers on which the code is executed (and if N is zero, whether a pool is started at all). If no pool exists, the code will execute on the client. Nevertheless, the code remains a parfor loop, which does not have the same semantics as a for loop - there are restrictions on the loop code for parfor loops that do not exist for for loops, and there is no guarantee on the order in which the loop iterations are executed.
When you use parfor, you're doing more than just saying "speed this up please". You're saying to MATLAB "I can guarantee to you that the iterations of this loop are independent, and can be executed in any order, so you will be OK if you try to parallelize it". Because you've guaranteed that, MATLAB is able to speed things up by using different semantics than it would do for a for loop.
The only way to completely get for loop behaviour is to use for, and if you need to switch back and forth for debugging purposes you'll need to comment and uncomment the for/parfor (or perhaps use an if/else block, switching between a for and a parfor depending on some variable).
I think that the way to go here, is not to disable the parfor, but rather to let it behave like a simple for.
This should be possible by setting the number of workers to 1.
parpool(1)
Depending on your code you may be able to just do this once before you run the code, or perhaps you need to do this (conditionally) each time when you set the number of workers anywhere in your code.
I have two for loops running in my Matlab code. The inner loop is parallelized using Matlabpool in 12 processors (which is maximum Matlab allows in a single machine).
I dont have Distributed computing license. Please help me how to do it using Octave or Scilab. I just want to parallelize 'for' loop ONLY.
There are some broken links given while I searched for it in google.
parfor is not really implemented in octave yet. The keyword is accepted, but is a mere synonym of for (http://octave.1599824.n4.nabble.com/Parfor-td4630575.html).
The pararrayfun and parcellfun functions of the parallel package are handy on multicore machines.
They are often a good replacement to a parfor loop.
For examples, see
http://wiki.octave.org/Parallel_package.
To install, issue (just once)
pkg install -forge parallel
And then, once on each session
pkg load parallel
before using the functions
In Scilab you can use parallel_run:
function a=g(arg1)
a=arg1*arg1
endfunction
res=parallel_run(1:10, g);
Limitations
uses only one core on Windows platforms.
For now, parallel_run only handles arguments and results of scalar matrices of real values and the types argument is not used
one should not rely on side effects such as modifying variables from outer scope : only the data stored into the result variables will be copied back into the calling environment.
macros called by parallel_run are not allowed to use the JVM
no stack resizing (via gstacksize() or via stacksize()) should take place during a call to parallel_run
In GNU Octave you can use the parfor construct:
parfor i=1:10
# do stuff that may run in parallel
endparfor
For more info: help parfor
To see a list of Free and Open Source alternatives to MATLAB-SIMULINK please check its Alternativeto page or my answer here. Specifically for SIMULINK alternatives see this post.
something you should consider is the difference between vectorized, parallel, concurrent, asynchronous and multithreaded computing. Without going much into the details vectorized programing is a way to avoid ugly for-loops. For example map function and list comprehension on Python is vectorised computation. It is the way you write the code not necesarily how it is being handled by the computer. Parallel computation, mostly used for GPU computing (data paralleism), is when you run massive amount of arithmetic on big arrays, using GPU computational units. There is also task parallelism which mostly refers to ruing a task on multiple threads, each processed by a separate CPU core. Concurrent or asynchronous is when you have just one computational unit, but it does multiple jobs at the same time, without blocking the processor unconditionally. Basically like a mom cooking and cleaning and taking care of its kid at the same time but doing only one job at the time :)
Given the above description there are lot in the FOSS world for each one of these. For Scilab specifically check this page. There is MPI interface for distributed computation (multithreading/parallelism on multiple computers). OpenCL interfaces for GPU/data-parallel computation. OpenMP interface for multithreading/task-parallelism. The feval functions is not parallelism but a way to vectorize a conventional function.Scilab matrix arithmetic and parallel_run are vectorized or parallel depending to the platform, hardware and version of the Scilab.
I have a quad-core desktop computer
I have the Parallel Computing toolbox in Matlab.
I have a script file that I need to run simultaneously on each core
I'm not sure what the most efficient way to do this is, I know I can create a 'matlabpool' with 4 local workers, but how do I then assign the same script to each one? Or can I use the 'batch' command to run the script on a specific thread, then do that for each one?
Thank you!
You can run a single script using multiple cores using the Parallel Computing toolbox, by using
matlabpool open local 4
and using parfor instead of for loops to execute whatever is in your loop across four threads. I'm not sure if Parallel Computing toolbox supports running the entirety of the script individually on each core, this will likely not be supported by your hardware.
Not sure if this works, but here is something to try:
When trying to paralelize calculations, they are usually wrapped with something like parfor
So I would recommend doing the same with your script, make sure that all required inputs and outputs have the neccesary dimensions and just call:
parfor ii = 1:4
myscript;
end
Sidenote: Before trying this kind of stuff you may want to check your cpu utilization. If it is already high that means that the inner part of the code uses parallel processing and you should not expect much speedup.
I am working on a time series based calculation. Each iteration of the calculation is independent. Could anyone share some tips / online primers on using utilising parallel processing in Matlab? How can this be specified inside the actual code?
Since you have access to the Parallel toolbox, I suggest that you first check whether you can do it the easy way.
Basically, instead of writing
for i=1:lots
out(:,i)=do(something);
end
You write
parfor i=1:lots
out(:,i)=do(something);
end
Then, you use matlabpool to create a number of workers (you can have a maximum of 8 on your local machine with the toolbox, and tons on a remote cluster if you also have a Distributed Computing Server license), and you run the code, and see nice speed gains when your iterations are run by 8 cores instead of one.
Even though the parfor route is the easiest, it may not work right out of the box, since you might do your indexing wrong, or you may be referencing an array in a problematic way etc. Look at the mlint warnings in the editor, read the documentation, and rely on good old trial and error, and you should figure it out reasonably fast. If you have nested loops, it's often best parallelize only the innermost one and ensure it does tons of iterations - this is not only good design, it also reduces the amount of code that could give you trouble.
Note that especially if you run the code on a local machine, you may run into memory issues (which might manifest in really slow execution in parallel mode because you're paging): Every worker gets a copy of the workspace, so if your calculation involves creating a 500MB array, 8 workers will need a total 4GB of RAM - and then you haven't even started counting the RAM of the parent process! In addition, it can be good to only use N-1 cores on your machine, so that there is still one core left for other processes that may run on the computer (such as a mandatory antivirus...).
Mathworks offers its own parallel computing toolbox. If you do not want to purchase that, there a few options
You could write your own mex file and use pthreads or OpenMP.
However make sure you do not call any Mex api in the parallel part of the code, because they arent thread safe
If you want coarser grained parallelism via MPI you can try pmatlab
Same with parmatlab
Edit: Adding link Parallel MATLAB with openmp mex files
I have only tried the first.
Don't forget that many Matlab functions are already multithreaded. By careful programming you may be able to take advantage of them -- check the documentation for your version as the Mathworks seem to be increasing the range and number of multithreaded functions with each new release. For example, it seems that 2010a has multithreaded ffts which may be useful for time series processing.
If the intrinsic multithreading is not what you need, then as #srean suggests, the Parallel Computing Toolbox is available. For my money (or rather, my employers' money) it's the way to go, allowing you to program in parallel in Matlab, rather than having to bolt things on. I have to admit, too, that I'm quite impressed by the toolbox and the facilities it offers.