You can evaluate a function in the background without waiting
for it to complete, using
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
parfevalOnAll. This can be useful if
you want to be able to plot intermediate results. Note that this is
different from using
where you have to wait for the loop to complete.
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.
automatically invoke functions after each or after all elements of a
complete. This array can contain futures returned by
|Run function on parallel pool worker|
|Execute function asynchronously on all workers in parallel pool|
|Start counting bytes transferred within parallel pool|
|Read how many bytes have been transferred since calling
|Send data from worker to client using a data queue|
|Retrieve data sent from a worker|
|Define a function to call when new data is received on a DataQueue|
|Run function after each function finishes running in the background|
|Run function after all functions finish running in the background|
|Retrieve results from function running in the background|
|Retrieve next unread outputs from |
|Stop function running in the background|
|Wait for futures to complete|
|Function scheduled to run|
|Parallel pool of workers|
|Send and listen for data between client and workers|
|Send and poll data between client and workers|
Break out of a loop early and collect results as they become available.
This example shows how to query the state of
parfeval futures and
Automatically run functions after
Future objects finish running on
This example shows how to perform a parallel parameter sweep with
parfeval and send results back during computations with a
This example shows how to update a user interface as computations complete.
This example shows how to perform frame acquisition from a webcam in parallel with data postprocessing.
This example shows how to perform image acquisition from a webcam and postprocess data in parallel.
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.
Compare and contrast
spmd against other parallel computing
functionality such as