parforon workers in a parallel pool
Computing Toolbox™ supports interactive parallel
computing and enables you to accelerate your workflow by running on
multiple workers in a parallel pool. Use
for-loop iterations in parallel on workers
in a parallel pool. When you have profiled your code and identified
increase your throughput. Develop
on your desktop and scale up to a cluster without changing your code.
|Execute for-loop iterations in parallel on workers|
|Options for parfor, such as partitioning iterations|
|Create parallel pool on cluster|
|Execute function asynchronously on parallel pool worker|
|Start counting bytes transferred within parallel pool|
|Read how many bytes have been transferred since calling ticBytes|
|Send data from worker to client using a data queue|
|Define a function to call when new data is received on a DataQueue|
|Access parallel pool|
|Class that enables sending and listening for data between client and workers|
Discover basic concepts of a
and decide when to use it.
Diagnose and fix common
Iterations have no guaranteed order.
Learn how to deal with parallel nested loops.
Discover variable requirements and classification
Convert a slow
for-loop into a
Create arrays inside or outside
to speed up code.
Learn about starting and stopping parallel pools, pool size, and cluster selection.
Specify your preferences, and automatically create a parallel pool.
Discover how to use objects, handles, and sliced variables
All references to variables in
must be visible in the body of the program.
parfor-loops on your
desktop, and scale up to a cluster without changing your code.
You can use
parfor-loops to calculate
cumulative values that are updated by each iteration.
Control random number generation in
by assigning a particular substream for each iteration.
This example shows how to use a
parfor loop to perform a parameter sweep on a training option.