# Cook Until Done: parallel_do

For some loops, the end of the iteration space is not known in advance, or the loop body may add more iterations to do before the loop exits. You can deal with both situations using the template class tbb::parallel_do.

A linked list is an example of an iteration space that is not known in advance. In parallel programming, it is usually better to use dynamic arrays instead of linked lists, because accessing items in a linked list is inherently serial. But if you are limited to linked lists, the items can be safely processed in parallel, and processing each item takes at least a few thousand instructions, you can use `parallel_do` to gain some parallelism.

For example, consider the following serial code:

    void SerialApplyFooToList( const std::list<Item>& list ) {
        for( std::list<Item>::const_iterator i=list.begin() i!=list.end(); ++i )
            Foo(*i);
    }

If `Foo` takes at least a few thousand instructions to run, you can get parallel speedup by converting the loop to use `parallel_do`. To do so, define an object with a `const` qualified `operator()`. This is similar to a C++ function object from the C++ standard header `<functional>`, except that `operator()` must be `const`.

    class ApplyFoo {
    public:
        void operator()( Item& item ) const {
            Foo(item);
        }
    };

The parallel form of `SerialApplyFooToList` is as follows:

    void ParallelApplyFooToList( const std::list<Item>& list ) {
        parallel_do( list.begin(), list.end(), ApplyFoo() );
    }

An invocation of `parallel_do` never causes two threads to act on an input iterator concurrently. Thus typical definitions of input iterators for sequential programs work correctly. This convenience makes `parallel_do` unscalable, because the fetching of work is serial. But in many situations, you still get useful speedup over doing things sequentially.

There are two ways that `parallel_do` can acquire work scalably.

- The iterators can be random-access iterators.

- The body argument to `parallel_do`, if it takes a second argument feeder of type `parallel_do<Item>&`, can add more work by calling `feeder.add(item)`. For example, suppose processing a node in a tree is a prerequisite to processing its descendants. With `parallel_do`, after processing a node, you could use `feeder.add` to add the descendant nodes. The instance of `parallel_do` does not terminate until all items have been processed.

## Code Sample

The directory `examples/parallel_do/parallel_preorder` contains a small application that uses `parallel_do` to perform parallel preorder traversal of an acyclic directed graph. The example shows how `parallel_do_feeder` can be used to add more work.

Parent topic: Parallelizing Complex Loops