• 2021.4
• 09/27/2021
• Public Content
Contents

# Divide and Conquer

Problem
Parallelize a divide and conquer algorithm.
Context
Divide and conquer is widely used in serial algorithms. Common examples are quicksort and mergesort.
Forces
• Problem can be transformed into subproblems that can be solved independently.
• Splitting problem or merging solutions is relatively cheap compared to cost of solving the subproblems.
Solution
There are several ways to implement divide and conquer in Intel® oneAPI Threading Building Blocks (oneTBB). The best choice depends upon circumstances.
• If division always yields the same number of subproblems, use recursion and
oneapi::tbb::parallel_invoke
.
• If the number of subproblems varies, use recursion and
.
Example
Quicksort is a classic divide-and-conquer algorithm. It divides a sorting problem into two subsorts. A simple serial version looks like .
``````void SerialQuicksort( T* begin, T* end ) {
if( end-begin>1  ) {
using namespace std;
T* mid = partition( begin+1, end, bind2nd(less<T>(),*begin) );
swap( *begin, mid[-1] );
SerialQuicksort( begin, mid-1 );
SerialQuicksort( mid, end );
}
}``````
The number of subsorts is fixed at two, so
oneapi::tbb::parallel_invoke
provides a simple way to parallelize it. The parallel code is shown below:
``````void ParallelQuicksort( T* begin, T* end ) {
if( end-begin>1 ) {
using namespace std;
T* mid = partition( begin+1, end, bind2nd(less<T>(),*begin) );
swap( *begin, mid[-1] );
oneapi::tbb::parallel_invoke( [=]{ParallelQuicksort( begin, mid-1 );},
[=]{ParallelQuicksort( mid, end );} );
}
}``````
Eventually the subsorts become small enough that serial execution is more efficient. The following variation, does sorts of less than 500 elements using the earlier serial code.
``````void ParallelQuicksort( T* begin, T* end ) {
if( end-begin>=500 ) {
using namespace std;
T* mid = partition( begin+1, end, bind2nd(less<T>(),*begin) );
swap( *begin, mid[-1] );
oneapi::tbb::parallel_invoke( [=]{ParallelQuicksort( begin, mid-1 );},
[=]{ParallelQuicksort( mid, end );} );
} else {
SerialQuicksort( begin, end );
}
}``````
The change is an instance of the Agglomeration pattern.
The next example considers a problem where there are a variable number of subproblems. The problem involves a tree-like description of a mechanical assembly. There are two kinds of nodes:
• Leaf nodes represent individual parts.
• Internal nodes represent groups of parts.
The problem is to find all nodes that collide with a target node. The following code shows a serial solution that walks the tree. It records in
Hits
any nodes that collide with
Target
.
``````std::list<Node*> Hits;
Node* Target;

void SerialFindCollisions( Node& x ) {
if( x.is_leaf() ) {
if( x.collides_with( *Target ) )
Hits.push_back(&x);
} else {
for( Node::const_iterator y=x.begin();y!=x.end(); ++y )
SerialFindCollisions(*y);
}
}``````
A parallel version is shown below.
``````typedef oneapi::tbb::enumerable_thread_specific<std::list<Node*> > LocalList;
LocalList LocalHits;
Node* Target;    // Target node

void ParallelWalk( Node& x ) {
if( x.is_leaf() ) {
if( x.collides_with( *Target ) )
LocalHits.local().push_back(&x);
} else {
// Recurse on each child y of x in parallel
for( Node::const_iterator y=x.begin(); y!=x.end(); ++y )
g.run( [=]{ParallelWalk(*y);} );
// Wait for recursive calls to complete
g.wait();
}
}

void ParallelFindCollisions( Node& x ) {
ParallelWalk(x);
for(LocalList::iterator i=LocalHits.begin();i!=LocalHits.end(); ++i)
Hits.splice( Hits.end(), *i );
}``````
The recursive walk is parallelized using class
to do recursive calls in parallel.
There is another significant change because of the parallelism that is introduced. Because it would be unsafe to update
Hits
concurrently, the parallel walk uses variable
LocalHits
to accumulate results. Because it is of type