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Parallel Computing Toolbox Matlab Download Crackl

Parallel Computing Toolbox MATLAB Download

Parallel Computing Toolbox is a MATLAB product that lets you perform parallel computations on multicore computers, GPUs, and computer clusters. It provides high-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms that enable you to parallelize MATLAB applications without CUDA or MPI programming. You can also use the toolbox with Simulink to run multiple simulations of a model in parallel.

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In this article, we will show you how to download and install Parallel Computing Toolbox for MATLAB, and how to use some of its features to speed up your MATLAB code.

How to Download and Install Parallel Computing Toolbox

To download and install Parallel Computing Toolbox, you need to have a valid MATLAB license and an internet connection. You can follow these steps:

  • Open MATLAB and go to the Home tab.

  • Click on the Add-Ons button and select Get Add-Ons.

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

  • Click on the Install button and follow the instructions on the screen.

  • Restart MATLAB after the installation is complete.

You can also download Parallel Computing Toolbox from the MathWorks website. You will need to log in with your MathWorks account and select your platform and release. Then, you can follow the instructions on the website to download and install the toolbox.

How to Use Parallel Computing Toolbox

Parallel Computing Toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes, as well as write your own parallel code using parallel for-loops, distributed arrays, GPU arrays, and more. Here are some examples of how to use Parallel Computing Toolbox to speed up your MATLAB code.

Using Parallel For-Loops

A parallel for-loop (parfor) is a simple way to run independent iterations of a loop in parallel on multiple workers (MATLAB computational engines) that run locally or on a cluster. You can use parfor to perform tasks such as parameter sweeps, optimizations, and Monte Carlo simulations. For example, suppose you want to estimate the value of pi using a Monte Carlo method. You can use parfor to run multiple trials in parallel and average the results:

n = 1e7; % number of points per trial trials = 100; % number of trials tic; parfor i = 1:trials % generate n random points in the unit square x = rand(n,1); y = rand(n,1); % count how many points are inside the unit circle k = sum(x.^2 + y.^2 <= 1); % estimate pi for each trial pi_est(i) = 4*k/n; end % compute the mean and standard deviation of pi_est pi_mean = mean(pi_est); pi_std = std(pi_est); toc; fprintf('Mean estimate of pi: %f\n', pi_mean); fprintf('Standard deviation of pi: %f\n', pi_std);

The parfor loop automatically creates a parallel pool of workers and distributes the iterations among them. You can use the Parallel Pool tab in the MATLAB desktop to monitor the progress and performance of the parallel computations. You can also use the gcp function to access and modify the properties of the parallel pool, such as the number of workers, the cluster profile, and the idle timeout.

Using GPU Arrays

A GPU array is a special type of array that resides in the memory of a GPU device. You can use GPU arrays to perform computations on NVIDIA GPUs directly from MATLAB without having to write any CUDA code. More than 500 MATLAB functions run automatically on GPU arrays, including fft, element-wise operations, and several linear algebra operations such as lu and mldivide (also known as the backslash operator). You can also use GPU arrays with parallel-enabled functions in other toolboxes, such as Deep Learning Toolbox. For example, suppose you want to solve a system of linear equations of the form Ax=b using mldivide on a GPU. You can use gpuArray to create GPU arrays from regular MATLAB arrays:

n = 1000; % size of A and b A = rand(n); % create a random matrix A b = rand(n,1); % create a random vector b tic; x = A\b; % solve Ax=b on the CPU toc; fprintf('Time to solve Ax=b on the CPU: %f seconds\n', toc); % create GPU arrays from A and b A_gpu = gpuArray(A); b_gpu = gpuArray(b); tic; x_gpu = A_gpu\b_gpu; % solve Ax=b on the GPU toc; fprintf('Time to solve Ax=b on the GPU: %f seconds\n', toc); % check the accuracy of the solution err = norm(x - gather(x_gpu))/norm(x); fprintf('Relative error between CPU and GPU solutions: %e\n', err);

The gpuArray function transfers the data from the CPU memory to the GPU memory. The mldivide function then executes on the GPU and returns a GPU array. The gather function transfers the data from the GPU memory back to the CPU memory. You can use the gputimeit function to measure the execution time of a function on the GPU.


In this article, we have shown you how to download and install Parallel Computing Toolbox for MATLAB, and how to use some of its features to speed up your MATLAB code. Parallel Computing Toolbox is a powerful tool that lets you take advantage of multicore processors, GPUs, and computer clusters without requiring any low-level programming. You can use parallel for-loops, GPU arrays, distributed arrays, and other parallel constructs to perform computationally and data-intensive tasks in parallel. You can also use parallel-enabled functions in MATLAB and other toolboxes to automatically run your code in parallel. For more information and examples, you can visit the Parallel Computing Toolbox documentation and the Get Started with Parallel Computing Toolbox tutorial.


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