I'm new about parallel computing in matlab. I have a function which creates a classifiers (SVM) and I'd like to test it with several dataset. I've got a 2 core workstation so I'd like to run test in parallel. Can someone explain me the difference between:
dataset_array={dataset1, dataset2}
matlabpool open 2
spmd
my_function(dataset(labindex));
end
and
dataset_array={dataset1, dataset2}
matlabpool open 2
parfor i:1=2
my_function(dataset(i));
end
spmd is a parallel region, while parfor is a parallel for loop. The difference is that in spmd region you have a much larger flexibility when it comes to the tasks you can perform in parallel. You can write a for loop, you can operate on distributed arrays and vectors. You can program an entire work flow, which in general consists of more than loops. This comes at a price: you need to know more about distributing the work and the data among your threads. Parallelizing the loop for example requires explicitly dividing the loop index ranges amongst the workers (which you did in your code by using labindex), and maybe creating distributed arrays.
parfor on the other hand only does this - a parallelized for loop. Automatically parallelized, you can add, so the work is divided between the workers by MATLAB.
If you only want to run a single loop in parallel and later work on the result on your local client, you should use parfor. If you want to parallelize your entire MATLAB program, you will have to deal with the complexities of spmd and work distribution.
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A parallel computing problem in matlab. I want to communicate between my labs, so I should use SPMD. If I need to run many generations to end, can I only put SPMD into the for loop. I realized that it would be very slow to do so, because I repeatedly enabled the SPMD of maxgen times. The reality is that I don't know how to effectively combine SPMD with circulation.
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Suppose that I want run my code using a parfor loop in MATLAB R2016a. first four iterations of this loop (1:4) has higher computational load comparing to second four iterations (5:8) so I have high differences in calculation time of these two parts.
How we can have two parfor loops simultaneously in this case? For example second part of parfor loop (5:8) is doing it's job without waiting for completion of first part (1:4)? I want reduce the IDLE times using a new coding structure or any other tricks.
I have a laptop with a multi-core processor and I would like to run a lengthy loop in which Simulink simulations are performed. Is it beneficial to split the loop into two parts (it is possible in my case), open the Matlab application twice, and run a Matlab script in each of them?
Someone told me that Matlab/Simulink always uses one core per opened Matlab application. Is that correct?
MATLAB splits some builtin functions across multiple cores, but standard MATLAB code uses just one core. Generally, if you are running several independent iterations, then the computation time can benefit from parallelization. You can do this easily using either parfor (if the have the Parallel Computing Toolbox), or batch_job.
I have a kinetic monte carlo code. Now its kinetic and hence each loop updates the current state to a future state, making it to be a dependent for loop.
I want to use parallel computing feature of matlab, but it seems the famous 'parfor' command only works for independent loops.
So my question, is it possible to use parallel computing in matlab to parallelize code where loops are not independent?
Usually these kinds of calculations are done on a grid, and the grid is distributed across the workers, each worker having its own part of the grid to calculate. This can't be done independently in general because the value at one point on the grid will depend on neighbouring points. These boundary values are communicated between the workers using some mechanism such as message passing or shared memory.
In MATLAB you can either use spmd or communicating jobs with the labSend and labReceive functions or you can use distributed arrays.
I understand that you can use a matlabpool and parfor to run for loop iterations in parallel, however, I want to try and take advantage of using the high number of cores in my GPU to run a larger number of simultaneous iterations. I was wondering if there is any built in functionality to do this?
To my understanding, the method in which MATLAB runs code on the GPU is through a GPUarray, but that does not seem to parallelize a loop, only certain functions inside the loop.
For the loop that I am running, each iteration can run independently and the only variables that need to exist outside of the loop is the data to be processed (a 3-D array, where the first index is time, and each iteration is operating on a different time) and a 2-D output array where each iteration is storing the result for a particular time. Each time is independent.
Thanks
With a GPUArray, you can run elementwise operations in parallel by structuring your algorithm in terms of MATLAB's arrayfun. Effectively, this implicitly loops over each element of your arrays, and can apply the body of a MATLAB function to each element. The doc is: here.
There's a simple demo: here.