# parallel_for Template Function¶

## Summary¶

Template function that performs parallel iteration over a range of values.

## Syntax¶

template<typename Index, typename Func>
Func parallel_for( Index first, Index_type last, const Func& f

template<typename Index, typename Func>
Func parallel_for( Index first, Index_type last,
Index step, const Func& f

template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body,


## Description¶

A parallel_for(first,last,step,f) represents parallel execution of the loop:

for( auto i=first; i<last; i+=step ) f(i);


The index type must be an integral type. The loop must not wrap around. The step value must be positive. If omitted, it is implicitly 1. There is no guarantee that the iterations run in parallel. Deadlock may occur if a lesser iteration waits for a greater iteration. The partitioning strategy is auto_partitioner when the parameter is not specified.

A parallel_for(range,body,partitioner) provides a more general form of parallel iteration. It represents parallel execution of body over each value in range. The optional partitioner specifies a partitioning strategy. Type Range must model the Range concept . The body must model the requirements in the following table.

Requirements for parallel_for Body: Pseudo-Signature, Semantics

Body::Body( const Body& )

Copy constructor.

Body::~Body()

Destructor.

void Body::operator()( Range& range ) const

Apply body to range.

A parallel_for recursively splits the range into subranges to the point such that is_divisible() is false for each subrange, and makes copies of the body for each of these subranges. For each such body/subrange pair, it invokes Body::operator(). The invocations are interleaved with the recursive splitting, in order to minimize space overhead and efficiently use cache.

Some of the copies of the range and body may be destroyed after parallel_for returns. This late destruction is not an issue in typical usage, but is something to be aware of when looking at execution traces or writing range or body objects with complex side effects.

When worker threads are available, parallel_for executes iterations in non-deterministic order. Do not rely upon any particular execution order for correctness. However, for efficiency, do expect parallel_for to tend towards operating on consecutive runs of values.

When no worker threads are available, parallel_for executes iterations from left to right in the following sense. Imagine drawing a binary tree that represents the recursive splitting. Each non-leaf node represents splitting a subrange r by invoking one of the splitting constructors of Range. The left child represents the updated value of r. The right child represents the newly constructed object. Each leaf in the tree represents an indivisible subrange. The method Body::operator() is invoked on each leaf subrange, from left to right.

All overloads can be passed a task_group_context object so that the algorithm’s tasks are executed in this group. By default the algorithm is executed in a bound group of its own.

Complexity

If the range and body take O(1) space, and the range splits into nearly equal pieces, then the space complexity is O(P log(N)), where N is the size of the range and P is the number of threads.

## Example¶

This example defines a routine ParallelAverage that sets output[i] to the average of input[i-1], input[i], and input[i+1], for 1 <= i< n.

#include "tbb/parallel_for.h"
#include "tbb/blocked_range.h"

using namespace tbb;

struct Average {
const float* input;
float* output;
void operator()( const blocked_range<int>& range ) const {
for( int i=range.begin(); i!=range.end(); ++i )
output[i] = (input[i-1]+input[i]+input[i+1])*(1/3.f);
}
};

// Note: Reads input[0..n] and writes output[1..n-1].
void ParallelAverage( float* output, const float* input, size_t n ) {
Average avg;
avg.input = input;
avg.output = output;
parallel_for( blocked_range<int>( 1, n ), avg );
}


## Example¶

This example is more complex and requires familiarity with STL. It shows the power of parallel_for beyond flat iteration spaces. The code performs a parallel merge of two sorted sequences. It works for any sequence with a random-access iterator. The algorithm (Akl 1987) works recursively as follows:

• If the sequences are too short for effective use of parallelism, do a sequential merge. Otherwise perform steps 2-6.

• Swap the sequences if necessary, so that the first sequence [begin1,end1) is at least as long as the second sequence [begin2,end2).

• Set m1 to the middle position in [begin1,end1). Call the item at that location key.

• Set m2 to where key would fall in [begin2,end2).

• Merge [begin1,m1) and [begin2,m2) to create the first part of the merged sequence.

• Merge [m1,end1) and [m2,end2) to create the second part of the merged sequence.

The oneAPI Threading Building Blocks implementation of this algorithm uses the range object to perform most of the steps. Predicate is_divisible performs the test in step 1, and step 2. The splitting constructor does steps 3-6. The body object does the sequential merges.

#include "tbb/parallel_for.h"
#include <algorithm>

using namespace tbb;

template<typename Iterator>
struct ParallelMergeRange {
static size_t grainsize;
Iterator begin1, end1; // [begin1,end1) is 1st sequence to be merged
Iterator begin2, end2; // [begin2,end2) is 2nd sequence to be merged
Iterator out;               // where to put merged sequence
bool empty()   const {return (end1-begin1)+(end2-begin2)==0;}
bool is_divisible() const {
return std::min( end1-begin1, end2-begin2 ) > grainsize;
}
ParallelMergeRange( ParallelMergeRange& r, split ) {
if( r.end1-r.begin1 < r.end2-r.begin2 ) {
std::swap(r.begin1,r.begin2);
std::swap(r.end1,r.end2);
}
Iterator m1 = r.begin1 + (r.end1-r.begin1)/2;
Iterator m2 = std::lower_bound( r.begin2, r.end2, *m1 );
begin1 = m1;
begin2 = m2;
end1 = r.end1;
end2 = r.end2;
out = r.out + (m1-r.begin1) + (m2-r.begin2);
r.end1 = m1;
r.end2 = m2;
}
ParallelMergeRange( Iterator begin1_, Iterator end1_,
Iterator begin2_, Iterator end2_,
Iterator out_ ) :
begin1(begin1_), end1(end1_),
begin2(begin2_), end2(end2_), out(out_)
{}
};

template<typename Iterator>
size_t ParallelMergeRange<Iterator>::grainsize = 1000;

template<typename Iterator>
struct ParallelMergeBody {
void operator()( ParallelMergeRange<Iterator>& r ) const {
std::merge( r.begin1, r.end1, r.begin2, r.end2, r.out );
}
};

template<typename Iterator>
void ParallelMerge( Iterator begin1, Iterator end1, Iterator begin2, Iterator end2, Iterator out ) {
parallel_for(
ParallelMergeRange<Iterator>(begin1,end1,begin2,end2,out),
ParallelMergeBody<Iterator>(),
simple_partitioner()
);
}


Because the algorithm moves many locations, it tends to be bandwidth limited. Speedup varies, depending upon the system.