RAJA provides portable atomic operations that can be used to update values at arbitrary memory locations while avoiding data races. They are described in this section.

A complete working example code that shows RAJA atomic usage can be found in Computing a Histogram with Atomic Operations.


  • All RAJA atomic operations are in the namespace RAJA.

Atomic Operations

RAJA atomic support includes a variety of the most common atomic operations.


  • Each RAJA atomic operation is templated on an atomic policy.
  • Each method described in the table below returns the value of the potentially modified argument (i.e., *acc) immediately before the atomic operation is applied, in case it is needed by a user.
  • See Atomics for details about CUDA atomic operations.


  • atomicAdd< atomic_policy >(T* acc, T value) - Add value to *acc.
  • atomicSub< atomic_policy >(T* acc, T value) - Subtract value from *acc.


  • atomicMin< atomic_policy >(T* acc, T value) - Set *acc to min of *acc and value.
  • atomicMax< atomic_policy >(T* acc, T value) - Set *acc to max of *acc and value.


  • atomicInc< atomic_policy >(T* acc) - Add 1 to *acc.
  • atomicDec< atomic_policy >(T* acc) - Subtract 1 from *acc.
  • atomicInc< atomic_policy >(T* acc, T compare) - Add 1 to *acc if *acc < compare, else set *acc to zero.
  • atomicDec< atomic_policy >(T* acc, T compare) - Subtract 1 from *acc if *acc != 0 and *acc <= compare, else set *acc to compare.

Bitwise operations

  • atomicAnd< atomic_policy >(T* acc, T value) - Bitwise ‘and’ equivalent: Set *acc to *acc & value. Only works with integral data types.
  • atomicOr< atomic_policy >(T* acc, T value) - Bitwise ‘or’ equivalent: Set *acc to *acc | value. Only works with integral data types.
  • atomicXor< atomic_policy >(T* acc, T value) - Bitwise ‘xor’ equivalent: Set *acc to *acc ^ value. Only works with integral data types.


  • atomicExchange< atomic_policy >(T* acc, T value) - Replace *acc with value.
  • atomicCAS< atomic_policy >(T* acc, Tcompare, T value) - Compare and swap: Replace *acc with value if and only if *acc is equal to compare.

Here is a simple example that shows how to use an atomic operation to compute an integral sum on a CUDA GPU device:

// Use CUDA UM to share data pointer with host and device code.
// RAJA mechanics work the same way if device data allocation
// and host-device copies are done with traditional cudaMalloc
// and cudaMemcpy.
int* sum = nullptr;
cudaMallocManaged((void **)&sum, sizeof(int));
*sum = 0;

RAJA::forall< RAJA::cuda_exec >(RAJA::RangeSegment(0, N),
  [=] RAJA_DEVICE (RAJA::Index_type i) {

  RAJA::atomicAdd< RAJA::cuda_atomic >(sum, 1);


After this kernel executes, ‘*sum’ will be equal to ‘N’.


RAJA also provides an atomic interface similar to the C++20 ‘std::atomic_ref’, but which works for arbitrary memory locations. The class RAJA::AtomicRef provides an object-oriented interface to the atomic methods described above. For example, after the following operations:

double val = 2.0;
RAJA::AtomicRef<double,  RAJA::omp_atomic > sum(&val);

sum += 1.0;

the value of ‘val’ will be 5.

Atomic Policies

For more information about available RAJA atomic policies, please see Atomic Policies.

CUDA Atomics Architecture Dependencies

The internal implementations for RAJA atomic operations may vary depending on which CUDA architecture is available and/or specified when the RAJA is configured for compilation. The following rules apply when the following CUDA architecture level is chosen:

  • CUDA architecture is lower than `sm_35`
    • Certain atomics will be implemented using CUDA atomicCAS (Compare and Swap).
  • CUDA architecture is `sm_35` or higher
    • CUDA native 64-bit unsigned atomicMin, atomicMax, atomicAnd, atomicOr, atomicXor are used.
  • CUDA architecture is `sm_60` or higher
    • CUDA native 64-bit double atomicAdd is used.