Asynchronous Parallel Programming

Evaluate functions in the background using parfeval

You can evaluate a function in the background without waiting for it to complete, using parfeval. In many cases, it can be convenient to break out of a for loop early. For example, in an optimization procedure, you can stop the loop early when the result is good enough. You can do this on one or all parallel pool workers, using parfeval or parfevalOnAll. This can be useful if you want to be able to plot intermediate results. Using parfeval or parfevalOnAll is different from using parfor, where you have to wait for the loop to complete.

Use send and poll together to send and poll for messages or data from different workers using a data queue. You can use afterEach to add a function to call when new data is received from a data queue.

Use afterEach and afterAll to automatically invoke functions after each or after all elements of a Future array complete. This array can contain futures returned by parfeval, parfevalOnAll, afterEach or afterAll.

Functions

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Asynchronous Execution

`parfeval`	Run function on parallel pool worker
`parfevalOnAll`	Run function asynchronously on all workers in parallel pool
`Future`	Function scheduled to run
`afterEach`	Run function after each function finishes running in the background
`afterAll`	Run function after all functions finish running in the background
`fetchOutputs`	Retrieve results from function running in the background
`fetchNext`	Retrieve next unread outputs from `Future` array
`cancel`	Stop function running in the background
`cancelAll`	Cancel all jobs or tasks (Since R2022a)
`wait`	Wait for futures to complete
`parallel.Pool`	Parallel pool of workers

Data Queues

`send`	Send data between clients and workers using a data queue
`poll`	Retrieve data sent to pollable data queue
`close`	Close pollable data queue (Since R2025a)
`parallel.pool.DataQueue`	Send and listen for data between client and workers
`parallel.pool.PollableDataQueue`	Send and poll data between client and workers
`afterEach`	Define a function to call when new data is received on a data queue

Monitoring

`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling `ticBytes`

Topics

Get Started with Asynchronous Parallel Programming

Evaluate Functions in the Background Using parfeval
Break out of an optimizing loop early and collect results as they become available.
Use afterEach and afterAll to Run Callback Functions
Automatically run functions after Future objects finish running on parallel pools.
Query and Cancel parfeval Futures
This example shows how to query the state of parfeval futures and cancel them.
Update User Interface Asynchronously Using afterEach and afterAll
This example shows how to update a user interface as computations complete.
Choose Between spmd, parfor, and parfeval
Compare and contrast spmd against other parallel computing functionality such as parfor and parfeval.

Applications

Plot During Parameter Sweep with parfeval
This example shows how to perform a parallel parameter sweep with parfeval and send results back during computations with a DataQueue object.
Perform Webcam Image Acquisition in Parallel with Postprocessing
This example shows how to perform frame acquisition from a webcam in parallel with data postprocessing.
Perform Image Acquisition and Parallel Image Processing
This example shows how to perform image acquisition from a webcam and postprocess data in parallel.
Send Messages to Workers Using Pollable Data Queues
This example shows how to use pollable data queues to send data or instructions to workers during asynchronous function evaluations with parfeval. (Since R2025a)
Transfer Data Between Workers Using Pollable Data Queues
This example shows how to use a pollable data queue to transfer data between workers during asynchronous function evaluations with parfeval. (Since R2025a)
Train Deep Learning Networks in Parallel (Deep Learning Toolbox)
This example shows how to run multiple deep learning experiments on your local machine.
Use parfeval to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to use parfeval to perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training.
Receive Communication on Workers
This example shows how to set up a data queue on the workers to receive data. (Since R2023b)
Interactively Import and Process Data in Parallel
This example shows how to import and process data simultaneously in an interactive parallel pool. (Since R2023b)
Perform Data Acquisition and Processing on Pool Workers
This example shows how to implement a parallel data acquisition and processing pipeline on an interactive parallel pool. (Since R2025a)
Control Hardware and Acquire Data in Parallel
This example shows how to simultaneously control hardware and perform data acquisition on parallel workers. (Since R2025a)

Featured Examples

Plot During Parameter Sweep with parfeval

Perform a parallel parameter sweep with parfeval and send results back during computations with a DataQueue object.

Open Live Script

Train Deep Learning Networks in Parallel

Run multiple deep learning experiments on your local machine. Using this example as a template, you can modify the network layers and training options to suit your specific application needs. You can use this approach with a single or multiple GPUs. If you have a single GPU, the networks train one after the other in the background. The approach in this example enables you to continue using MATLAB® while deep learning experiments are in progress.

(Deep Learning Toolbox)

New

Send Messages to Workers Using Pollable Data Queues

Use pollable data queues to send data or instructions to workers during asynchronous function evaluations with parfeval.

Since R2025a
Open Live Script

Perform Webcam Image Acquisition in Parallel with Postprocessing

Perform frame acquisition from a webcam in parallel with data postprocessing.

Open Live Script

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