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How to Use Parallel Computing Toolbox in MATLAB to Speed Up Your Computations

If you are working with computationally and data-intensive problems in MATLAB, you might want to take advantage of the Parallel Computing Toolbox. This toolbox lets you perform parallel computations on multicore computers, GPUs, and clusters without having to write any CUDA or MPI code. You can use parallel for-loops, special array types, and parallelized numerical algorithms to parallelize your MATLAB applications. You can also use parallel-enabled functions in MATLAB and other toolboxes, such as Deep Learning Toolbox, to run your code faster on NVIDIA GPUs. In this article, we will show you how to download, install, and use the Parallel Computing Toolbox in MATLAB.

Download and Install Parallel Computing Toolbox

To download and install the Parallel Computing Toolbox, you need to have MATLAB R2023a (9.14) or later installed on your computer. You can get a free trial or purchase a license from the MathWorks website[^1^]. Once you have MATLAB installed, you can follow these steps to download and install the Parallel Computing Toolbox:

Open MATLAB and go to the Home tab.

Click on Add-Ons and select Get Add-Ons.

In the Add-On Explorer window, search for Parallel Computing Toolbox and click on it.

Click on Install and follow the instructions to complete the installation.

You can also download and install the Parallel Computing Toolbox from other sources, such as RuTracker[^2^] or Reddit[^3^], but be aware that these may not be official or safe sources. You may also need a crack or a serial number to activate the toolbox, which may violate the MathWorks terms of use.

Use Parallel Computing Toolbox

Once you have installed the Parallel Computing Toolbox, you can start using it in your MATLAB code. Here are some examples of how you can use it:

To run independent iterations of a for-loop in parallel on multicore CPUs, use parfor instead of for. For example:

% Serial for-loop

tic

for i = 1:10

pause(1) % Simulate some computation

end

toc

% Parallel for-loop

tic

parfor i = 1:10

pause(1) % Simulate some computation

end

toc

The parallel for-loop should run faster than the serial for-loop because it distributes the iterations across multiple workers (MATLAB computational engines) that run locally.

To run your code on NVIDIA GPUs, use gpuArray to create arrays that reside on the GPU memory. You can then use over 500 MATLAB functions that run automatically on GPUs, such as fft, lu, or mldivide. For example:

% Create a large matrix on CPU memory

A = rand(10000);

% Transfer it to GPU memory

A = gpuArray(A);

% Perform matrix inversion on GPU

tic

B = A \\ eye(10000);

toc

% Transfer the result back to CPU memory

B = gather(B);

The matrix inversion should run faster on GPU than on CPU because it uses parallelized numerical algorithms that leverage the GPU architecture.

To run your code on clusters or clouds, use batch to submit your code as a batch job to a cluster or cloud service that has MATLAB Parallel Server installed. For example:

% Create a cluster object

c = parcluster('mycluster');

% Submit a batch job that runs a function 'myfun' with input arguments 'a' and 'b'

job = batch(c,@myfun,1,{a,b});

% Wait for the job to finish

wait(job);

% Retrieve the output argument from the job

out = fetchOutputs(job);

The batch job should run faster than running the code locally because it uses multiple workers on remote machines that have more computing resources.

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