Asynchronous Parallel Programming
parfevalYou 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
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 afterFutureobjects finish running on parallel pools. - Query and Cancel parfeval Futures
This example shows how to query the state ofparfevalfutures 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 contrastspmdagainst other parallel computing functionality such asparforandparfeval.
Applications
- Plot During Parameter Sweep with parfeval
This example shows how to perform a parallel parameter sweep withparfevaland send results back during computations with aDataQueueobject. - 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 withparfeval. (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 withparfeval. (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 useparfevalto 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.
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.
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.
Perform Webcam Image Acquisition in Parallel with Postprocessing
Perform frame acquisition from a webcam in parallel with data postprocessing.
