Applications

Explore examples for accelerating simulations, solvers, statistical models, signal processing, and image processing

Parallel Computing Toolbox™ lets you solve compute- and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to scale MATLAB® applications without CUDA® or MPI programming.

Explore these examples to discover how parallel computing can reduce the time it takes to get results.

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Featured Examples

Parallel Simulations Using Parsim: Parameter Sweep in Normal Mode

Parallel Simulations Using Parsim: Parameter Sweep in Normal Mode

Run multiple simulations of a Monte Carlo study in parallel by using Parallel Computing Toolbox™. Parallel execution leverages the multiple cores of your host machine to run many simulations more quickly. These simulations could also be run in parallel on computer clusters using the MATLAB® Parallel Server™. This example will work even if the Parallel Computing Toolbox™ or the MATLAB Parallel Server is not available, but the simulations will run in serial.

(Simulink)

Optimize Simulink Model in Parallel

Optimize Simulink Model in Parallel

Optimize a Simulink model in parallel using several Global Optimization Toolbox solvers.

(Global Optimization Toolbox)

Train Classification Ensemble in Parallel

Train Classification Ensemble in Parallel

Train a bagged ensemble in parallel reproducibly.

(Statistics and Machine Learning Toolbox)

Accelerate Audio Machine Learning Workflows Using a GPU

Accelerate Audio Machine Learning Workflows Using a GPU

Use GPU computing to accelerate machine learning workflows for audio, speech, and acoustic applications.

(Audio Toolbox)

  • Since R2024a
Accelerate MIMO-OFDM Link Simulation Using GPU

Accelerate MIMO-OFDM Link Simulation Using GPU

Accelerate a MIMO-OFDM link simulation with LDPC channel coding by using a GPU.

(Communications Toolbox)

  • Since R2024a
Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers

Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers

Use parallel computing to extract signal multidomain features for bearing fault detection.

(Signal Processing Toolbox)

  • Since R2024b
Train Network Using Automatic Multi-GPU Support

Train Network Using Automatic Multi-GPU Support

Use multiple GPUs on your local machine for deep learning training using automatic parallel support.

(Deep Learning Toolbox)