MATLAB PARALLEL COMPUTING TOOLBOX - S Uživatelská příručka stáhnout pdf (Strana 32)

100

101

2 Parallel for-Loops (parfor)

2-4

Create a parfor-Loop

The safest approach when creating a parfor-loop is to assume that iterations are

performed on different MATLAB workers in the parallel pool, so there is no sharing

of information between iterations. If you have a for-loop in which all iterations are

completely independent of each other, this loop is a good candidate for a parfor-loop.

Basically, if one iteration depends on the results of another iteration, these iterations are

not independent and cannot be evaluated in parallel, so you cannot easily reprogram the

loop into a parfor-loop.

The following example produces equivalent results, with a for-loop on the left, and a

parfor-loop on the right. Try typing each in your MATLAB Command Window:

clear A

for i = 1:8

A(i) = i;

end

1 2 3 4 5 6 7 8

clear A

parfor i = 1:8

A(i) = i;

end

1 2 3 4 5 6 7 8

Notice that each element of A is equal to its index. The parfor-loop works because

each element depends only upon its iteration of the loop, and upon no other iterations.

for-loops that merely repeat such independent tasks are ideally suited candidates for

parfor-loops.

Note If a parallel pool is not running, parfor creates a pool using your default cluster

profile, if your parallel preferences are set accordingly.

Related Examples

• “Run Parallel for-Loops (parfor)” on page 1-4

• “Run a Batch Parallel Loop” on page 1-9

• “Convert Nested for-Loops to parfor” on page 2-48

More About

• “Parallel Pools” on page 2-44

• “Introduction to parfor” on page 2-2

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