Acceleration

Accelerate code processing in MATLAB^® and Simulink^®

To speed up your code while prototyping, you can:

Leverage multiple cores or workers by using the parfor function.
Transfer data to the GPU by using the gpuArray (Parallel Computing Toolbox) function and then retrieve the output data from the GPU by using the gather (Parallel Computing Toolbox) function. For a list of Communications Toolbox™ features, see Functions and System Objects Supporting GPU Arrays.
Run your simulation on a cloud or in a cluster with multiple workers. For more information, see MathWorks Cloud Center (Parallel Computing Toolbox).

Functions

`gather`	Transfer distributed array, `Composite` object, or `gpuArray` object to local workspace
`gpuArray`	Array stored on GPU
`parfor`	Parallel `for`-loop

Topics

Accelerate Simulation Using GPUs
Speed up your communications system simulation using GPUs.

Featured Examples

Accelerate MIMO-OFDM Link Simulation Using GPU

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

Since R2024a
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Simulation Acceleration Using MATLAB Coder and Parallel Computing Toolbox

Ways to accelerate the simulation of communications algorithms in MATLAB.

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Accelerate SISO Single-Carrier Link Simulation Using GPU

Compare four techniques to accelerate SISO single-carrier link bit error rate (BER) simulations.

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Using GPUs to Accelerate Turbo Coding Bit Error Rate Simulations

Use GPUs to dramatically accelerate bit error rate simulations.

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LDPC Link Simulation Using GPU Processing

Use the ldpcDecode function and gpuArray to increase the speed of a communications system simulation.

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5G LDPC Block Error Rate Simulation Using the Cloud or a Cluster

Use the cloud or a cluster for block error rate (BLER) simulation of low-density parity-check (LDPC) coding for the 5G NR downlink shared transport channel (DL-SCH).

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