parforon workers in a parallel pool
Parallel 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.
|Options set for |
|Create parallel pool on cluster|
|Run function on parallel pool worker|
|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|
|Define a function to call when new data is received on a DataQueue|
|Parallel pool of workers|
|Send and listen for data between client and workers|
Discover basic concepts of a
parfor-loop, 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 in
Convert a slow
for-loop into a
Create arrays inside or outside
parfor-loops to speed up
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 in
All references to variables in
parfor-loops 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
assigning a particular substream for each iteration.
This example shows how to use
parfor-loops to speed up Monte-Carlo
Use parfor to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to use a
parfor loop to perform a parameter sweep on a training option.