Reduction Operations

RAJA does not provide separate loop execution methods for loops containing reduction operations like some other C++ loop programming abstraction models. Instead, RAJA provides reduction types that allow users to perform reduction operations in RAJA::forall and RAJA::kernel kernels in a portable, thread-safe manner. Users may use as many reduction objects in a loop kernel as they need. Available RAJA reduction types are described in this section.

A detailed example of RAJA reduction usage can be found in Reductions.


All RAJA reduction types are located in the namespace RAJA.



  • Each RAJA reduction type is templated on a reduction policy and a reduction value type for the reduction variable. The reduction policy type must be compatibe with the execution policy used by the kernel. For example, in a CUDA kernel, a CUDA reduction policy must be used.
  • Each RAJA reduction type accepts an initial reduction value or values at construction (see below).
  • Each RAJA reduction type has a ‘get’ method to access reduced values after kernel execution completes.

Reduction Types

RAJA supports five common reduction types:

  • ReduceSum< reduce_policy, data_type > - Sum of values.
  • ReduceMin< reduce_policy, data_type > - Min value.
  • ReduceMax< reduce_policy, data_type > - Max value.
  • ReduceMinLoc< reduce_policy, data_type > - Min value and a loop index where the minimum was found.
  • ReduceMaxLoc< reduce_policy, data_type > - Max value and a loop index where the maximum was found.

and two less common bitwise reduction types:

  • ReduceBitAnd< reduce_policy, data_type > - Bitwise ‘and’ of values (i.e., a & b).
  • ReduceBitOr< reduce_policy, data_type > - Bitwise ‘or’ of values (i.e., a | b).


  • When RAJA::ReduceMinLoc and RAJA::ReduceMaxLoc are used in a sequential execution context, the loop index of the min/max is the first index where the min/max occurs.
  • When these reductions are used in a parallel execution context, the loop index computed for the reduction value may be any index where the min or max occurs.


RAJA::ReduceBitAnd and RAJA::ReduceBitOr reduction types are designed to work on integral data types because in C++, at the language level, there is no such thing as a bitwise operator on floating-point numbers.

Reduction Examples

Next, we provide a few examples to illustrate basic usage of RAJA reduction types.

Here is a simple RAJA reduction example that shows how to use a sum reduction type and a min-loc reduction type:

const int N = 1000;

// Initialize array of length N with all ones. Then, set some other
// values in the array to make the example mildly interesting...
int vec[N] = {1};
vec[100] = -10; vec[500] = -10;

// Create a sum reduction object with initial value of zero
RAJA::ReduceSum< RAJA::omp_reduce, int > vsum(0);

// Create a min-loc reduction object with initial min value of 100
// and initial location index value of -1
RAJA::ReduceMinLoc< RAJA::omp_reduce, int > vminloc(100, -1);

// Run a kernel using the reduction objects
RAJA::forall<RAJA::omp_parallel_for_exec>( RAJA::RangeSegment(0, N),
  [=](RAJA::Index_type i) {

  vsum += vec[i];
  vminloc.minloc( vec[i], i );


// After kernel is run, extract the reduced values
int my_vsum = static_cast<int>(vsum.get());

int my_vmin = static_cast<int>(vminloc.get());
int my_vminloc = static_cast<int>(vminloc.getLoc());

The results of these operations will yield the following values:

  • my_vsum == 978 (= 998 - 10 - 10)
  • my_vmin == -10
  • my_vminloc == 100 or 500

Note that the location index for the minimum array value can be one of two values depending on the order of the reduction finalization since the loop is run in parallel. Also, note that the reduction objects are created using a RAJA::omp_reduce reduction policy, which is compatible with the OpenMP execution policy used in the kernel.

Here is an example of a bitwise or reduction:

const int N = 100;

// Initialize all entries in array of length N to the value '9'
int vec[N] = {9};

// Create a bitwise or reduction object with initial value of '5'
RAJA::ReduceBitOr< RAJA::omp_reduce, int > my_or(5);

// Run a kernel using the reduction object
RAJA::forall<RAJA::omp_parallel_for_exec>( RAJA::RangeSegment(0, N),
  [=](RAJA::Index_type i) {

  my_or |= vec[i];


// After kernel is run, extract the reduced value
int my_or_reduce_val = static_cast<int>(my_or.get());

The result of the reduction is the value ‘13’. In binary representation (i.e., bits), \(9 = ...01001\) (the vector entries) and \(5 = ...00101\) (the initial reduction value). So \(9 | 5 = ...01001 | ...00101 = ...01101 = 13\).

Reduction Policies

For more information about available RAJA reduction policies and guidance on which to use with RAJA execution policies, please see Reduction Policies.