Create a cluster object using the parcluster function, and create a set of parfor options with it. By default, parcluster uses your default cluster profile. Check your default profile on the MATLAB® Home tab, in Parallel > Select a Default Cluster.

cluster = parcluster;
opts = parforOptions(cluster);

To run parfor computations directly in the cluster, pass the parfor options as the second input argument to parfor.

When you use this approach, parfor can use all the available workers in the cluster, and workers become available as soon as the loop completes. This approach is also useful if your cluster does not support parallel pools.

values = [3 3 3 7 3 3 3];
parfor (i=1:numel(values),opts)
    out(i) = norm(pinv(rand(values(i)*1e3)));
end

Use this syntax to run parfor on a large cluster without consuming workers for longer than necessary.

You can control how parfor divides iterations into subranges for the workers with parforOptions. Controlling the range partitioning can optimize performance of a parfor-loop. For best performance, try to split into subranges that are:

To partition iterations into subranges of fixed size, create a set of parfor options, set 'RangePartitionMethod' to 'fixed', and specify a subrange size with 'SubrangeSize'.

opts = parforOptions(parcluster,'RangePartitionMethod','fixed','SubrangeSize',2);

Pass the parfor options as the second input argument to parfor. In this case, parfor divides iterations into three groups of 2 iterations.

values = [3 3 3 3 3 3];
parfor (i=1:numel(values),opts)
    out(i) = norm(pinv(rand(values(i)*1e3)));
end

To partition iterations into subranges of varying size, pass a function handle to the 'RangePartitionMethod' name-value pair. This function must return a vector of subrange sizes, and their sum must be equal to the number of iterations. For more information on this syntax, see method.

opts = parforOptions(parcluster,'RangePartitionMethod', @(n,nw) [2 1 1 2]);

Pass the parfor options as the second input argument to parfor. In this case, parfor divides iterations into four groups of 2, 1, 1, and 2 iterations.

values = [3 3 7 7 3 3];
parfor (i=1:numel(values),opts)
    out(i) = norm(pinv(rand(values(i)*1e3)));
end

You can use parforOptions to run parfor on the workers of a parallel pool. Use this approach when you want to reserve a fixed number of workers for the parfor-loop. You can also have finer control on how parfor divides iterations for workers.

Create a parallel pool using the parpool function. By default, parpool uses your default cluster profile. Check your default profile on the MATLAB Home tab, in Parallel > Select a Default Cluster. Create a set of parfor options with the parallel pool object, and specify options. For example, specify subranges of fixed size 2 as the partitioning method.

p = parpool;

Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).

opts = parforOptions(p,'RangePartitionMethod','fixed','SubrangeSize',2);

Pass the parfor options as the second input argument to the parfor function. parfor runs the loop body on the parallel pool and divides iterations according to opts.

values = [3 3 3 3 3 3];
parfor (i=1:numel(values),opts)
    out(i) = norm(pinv(rand(values(i)*1e3)));
end

When you run parfor with or without a parallel pool, by default, MATLAB performs an automatic dependency analysis on the loop body. MATLAB transfers required files to the workers before running the statements. In some cases, you must explicitly transfer those files to the workers. For more information, see Identify Program Dependencies.

If you are using parfor without a parallel pool, use parforOptions to transfer files. Create a cluster object using the parcluster option. Create a set of parfor options with the cluster object using the parforOptions function. To transfer files to the workers, use the 'AttachedFiles' name-value pair.

cluster = parcluster;
opts = parforOptions(cluster,'AttachedFiles',{'myFile.dat'});

Pass the parfor options as the second input argument to the parfor function. The workers can access the required files in the loop body.

parfor (i=1:2,opts)
    M = csvread('myFile.dat',0,2*(i-1),[0,2*(i-1),1,1+2*(i-1)]);
    out(i) = norm(rand(ceil(norm(M))*1e3));
end