Policies

RAJA kernel execution methods take an execution policy type template parameter to specialize execution behavior. Typically, the policy indicates which programming model back-end to use and other information about the execution pattern, such as number of CUDA threads per thread block, whether execution is synchronous or asynchronous, etc. This section describes RAJA policies for loop kernel execution, scans, sorts, reductions, atomics, etc. Please detailed examples in RAJA Tutorial and Examples for a variety of use cases.

As RAJA functionality evolves, new policies are added and some may be redefined and to work in new ways.

Note

• All RAJA policies are in the namespace RAJA.
• All RAJA policies have a prefix indicating the back-end implementation that they use; e.g., omp_ for OpenMP, cuda_ for CUDA, etc.

RAJA Loop/Kernel Execution Policies

The following tables summarize RAJA policies for executing kernels. Please see notes below policy descriptions for additional usage details and caveats.

Sequential CPU Policies

For the sequential CPU back-end, RAJA provides policies that allow developers to have some control over the optimizations that compilers are allow to apply during code compilation.

Sequential/SIMD Execution Policies	Works with	Brief description
seq_exec	forall, kernel (For), scan, sort	Strictly sequential execution.
simd_exec	forall, kernel (For), scan	Try to force generation of SIMD instructions via compiler hints in RAJA’s internal implementation.
loop_exec	forall, kernel (For), scan, sort	Allow the compiler to generate any optimizations that its heuristics deem beneficial according; i.e., no loop decorations (pragmas or intrinsics) in RAJA implementation.

OpenMP Parallel CPU Policies

For the OpenMP CPU multithreading back-end, RAJA has policies that can be used by themselves to execute kernels. In particular, they create an OpenMP parallel region and execute a kernel within it. To distinguish these in this discussion, we refer to these as full policies. These policies are provided to users for convenience in common use cases.

RAJA also provides other OpenMP policies, which we refer to as partial policies, since they need to be used in combination with other policies. Typically, they work by providing an outer policy and an inner policy as a template parameter to the outer policy. These give users flexibility to create more complex execution patterns.

Note

To control the number of threads used by OpenMP policies, set the value of the environment variable ‘OMP_NUM_THREADS’ (which is fixed for duration of run), or call the OpenMP routine ‘omp_set_num_threads(nthreads)’ in your application, which allows one to change the number of threads at run time.

The full policies are described in the following table. Partial policies are described in other tables below.

OpenMP CPU Full Policies	Works with	Brief description
omp_parallel_for_exec	forall, kernel (For), scan, sort	Same as applying ‘omp parallel for’ pragma
omp_parallel_for_static_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp parallel for schedule(static, ChunkSize)’
omp_parallel_for_dynamic_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp parallel for schedule(dynamic, ChunkSize)’
omp_parallel_for_guided_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp parallel for schedule(guided, ChunkSize)’
omp_parallel_for_runtime_exec	forall, kernel (For)	Same as applying ‘omp parallel for schedule(runtime)’

Note

For the OpenMP scheduling policies above that take a ChunkSize parameter, the chunk size is optional. If not provided, the default chunk size that OpenMP applies will be used, which may be specific to the OpenMP implementation in use. For this case, the RAJA policy syntax is omp_parallel_for_{static|dynamic|guided}_exec< >, which will result in the OpenMP pragma omp parallel for schedule({static|dynamic|guided}) being applied.

RAJA provides an (outer) OpenMP CPU policy to create a parallel region in which to execute a kernel. It requires an inner policy that defines how a kernel will execute in parallel inside the region.

OpenMP CPU Outer Policies	Works with	Brief description
omp_parallel_exec<InnerPolicy>	forall, kernel (For), scan	Creates OpenMP parallel region and requires an InnerPolicy. Same as applying ‘omp parallel’ pragma.

Finally, we summarize the inner policies that RAJA provides for OpenMP. These policies are passed to the RAJA omp_parallel_exec outer policy as a template argument as described above.

OpenMP CPU Inner Policies	Works with	Brief description
omp_for_exec	forall, kernel (For), scan	Parallel execution within existing parallel region; i.e., apply ‘omp for’ pragma.
omp_for_static_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp for schedule(static, ChunkSize)’
omp_for_nowait_static_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp for schedule(static, ChunkSize) nowait’
omp_for_dynamic_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp for schedule(dynamic, ChunkSize)’
omp_for_guided_exec<ChunkSize>	forall, kernel (For)	Same as applying ‘omp for schedule(guided, ChunkSize)’
omp_for_runtime_exec	forall, kernel (For)	Same as applying ‘omp for schedule(runtime)’
omp_parallel_collapse_exec	kernel (Collapse + ArgList)	Use in Collapse statement to parallelize multiple loop levels in loop nest indicated using ArgList

Important

RAJA only provides a nowait policy option for static scheduling since that is the only schedule case that can be used with nowait and be correct in general when executing multiple loops in a single parallel region. Paraphrasing the OpenMP standard: programs that depend on which thread executes a particular loop iteration under any circumstance other than static schedule are non-conforming.

Note

As in the RAJA full policies for OpenMP scheduling, the ChunkSize is optional. If not provided, the default chunk size that the OpenMP implementation applies will be used.

Note

As noted above, RAJA inner OpenMP policies must only be used within an existing parallel region to work properly. Embedding an inner policy inside the RAJA outer omp_parallel_exec will allow you to apply the OpenMP execution prescription specified by the policies to a single kernel. To support use cases with multiple kernels inside an OpenMP parallel region, RAJA provides a region construct that takes a template argument to specify the execution back-end. For example:

RAJA::region<RAJA::omp_parallel_region>([=]() {

  RAJA::forall<RAJA::omp_for_nowait_static_exec< > >(segment,
    [=] (int idx) {
      // do something at iterate 'idx'
    }
  );

  RAJA::forall<RAJA::omp_for_static_exec< > >(segment,
    [=] (int idx) {
      // do something else at iterate 'idx'
    }
  );

});

Here, the RAJA::region<RAJA::omp_parallel_region> method call creates an OpenMP parallel region, which contains two RAJA::forall kernels. The first uses the RAJA::omp_for_nowait_static_exec< > policy, meaning that no thread synchronization is needed after the kernel. Thus, threads can start working on the second kernel while others are still working on the first kernel. I general, this will be correct when the segments used in the two kernels are the same, each loop is data parallel, and static scheduling is applied to both loops. The second kernel uses the RAJA::omp_for_static_exec policy, which means that all threads will complete before the kernel exits. In this example, this is not really needed since there is no more code to execute in the parallel region and there is an implicit barrier at the end of it.

Threading Building Block (TBB) Parallel CPU Policies

RAJA provides a basic set of TBB execution policies for use with the RAJA TBB back-end, which supports a subset of RAJA features.

Threading Building Blocks Policies	Works with	Brief description
tbb_for_exec	forall, kernel (For), scan	Execute loop iterations. as tasks in parallel using TBB parallel_for method.
tbb_for_static<CHUNK_SIZE>	forall, kernel (For), scan	Same as above, but use. a static scheduler with given chunk size.
tbb_for_dynamic	forall, kernel (For), scan	Same as above, but use a dynamic scheduler.

Note

To control the number of TBB worker threads used by these policies: set the value of the environment variable ‘TBB_NUM_WORKERS’ (which is fixed for duration of run), or create a ‘task_scheduler_init’ object:

tbb::task_scheduler_init TBBinit( nworkers );

// do some parallel work

TBBinit.terminate();
TBBinit.initialize( new_nworkers );

// do some more parallel work

This allows changing number of workers at run time.

GPU Policies for CUDA and HIP

RAJA policies for GPU execution using CUDA or HIP are essentially identical. The only difference is that CUDA policies have the prefix cuda_ and HIP policies have the prefix hip_.

CUDA/HIP Execution Policies	Works with	Brief description
cuda/hip_exec<BLOCK_SIZE>	forall, scan, sort	Execute loop iterations in a GPU kernel launched with given thread-block size. Note that the thread-block size must be provided, there is no default provided.
cuda/hip_thread_x_direct	kernel (For)	Map loop iterates directly to GPU threads in x-dimension, one iterate per thread (see note below about limitations)
cuda/hip_thread_y_direct	kernel (For)	Same as above, but map to threads in y-dim
cuda/hip_thread_z_direct	kernel (For)	Same as above, but map to threads in z-dim
cuda/hip_thread_x_loop	kernel (For)	Similar to thread-x-direct policy, but use a block-stride loop which doesn’t limit number of loop iterates
cuda/hip_thread_y_loop	kernel (For)	Same as above, but for threads in y-dimension
cuda/hip_thread_z_loop	kernel (For)	Same as above, but for threads in z-dimension
cuda/hip_flatten_block_threads_{xyz}	Launch (Loop)	Reshapes threads in a multi-dimensional thread team into one-dimension, accepts any permutation of dimensions (expt namespace)
cuda/hip_block_x_direct	kernel (For)	Map loop iterates directly to GPU thread blocks in x-dimension, one iterate per block
cuda/hip_block_y_direct	kernel (For)	Same as above, but map to blocks in y-dimension
cuda/hip_block_z_direct	kernel (For)	Same as above, but map to blocks in z-dimension
cuda/hip_block_x_loop	kernel (For)	Similar to block-x-direct policy, but use a grid-stride loop.
cuda/hip_block_y_loop	kernel (For)	Same as above, but use blocks in y-dimension
cuda/hip_block_z_loop	kernel (For)	Same as above, but use blocks in z-dimension
cuda/hip_global_thread_x	Launch (Loop)	Creates a unique thread id for each thread on x-dimension of the grid (expt namespace)
cuda/hip_global_thread_y	Launch (Loop)	Same as above, but uses threads in y-dimension (expt namespace)
cuda/hip_global_thread_z	Launch (Loop)	Same as above, but uses threads in z-dimension (expt namespace)
cuda/hip_warp_direct	kernel (For)	Map work to threads in a warp directly. Cannot be used in conjunction with cuda/hip_thread_x_* policies. Multiple warps can be created by using cuda/hip_thread_y/z_* policies.
cuda/hip_warp_loop	kernel (For)	Policy to map work to threads in a warp using a warp-stride loop. Cannot be used in conjunction with cuda/hip_thread_x_* policies. Multiple warps can be created by using cuda/hip_thread_y/z_* policies.
cuda/hip_warp_masked_direct<BitMask<..>>	kernel (For)	Policy to map work directly to threads in a warp using a bit mask. Cannot be used in conjunction with cuda/hip_thread_x_* policies. Multiple warps can be created by using cuda/hip_thread_y/z_* policies.
cuda/hip_warp_masked_loop<BitMask<..>>	kernel (For)	Policy to map work to threads in a warp using a bit mask and a warp-stride loop. Cannot be used in conjunction with cuda/hip_thread_x_* policies. Multiple warps can be created by using cuda/hip_thread_y/z_* policies.
cuda/hip_block_reduce	kernel (Reduce)	Perform a reduction across a single GPU thread block.
cuda/_warp_reduce	kernel (Reduce)	Perform a reduction across a single GPU thread warp.

Several notable constraints apply to RAJA CUDA/HIP thread-direct policies.

Note

• Repeating thread direct policies with the same thread dimension in perfectly nested loops is not recommended. Your code may do something, but likely will not do what you expect and/or be correct.
• If multiple thread direct policies are used in a kernel (using different thread dimensions), the product of sizes of the corresponding iteration spaces cannot be greater than the maximum allowable threads per block. Typically, this is 1024 threads per block. Attempting to execute a kernel with more than the maximum allowed the CUDA runtime to complain about illegal launch parameters.
• Thread-direct policies are recommended only for certain loop patterns, such as tiling.

Several notes regarding CUDA/HIP thread and block loop policies are also good to know.

Note

• There is no constraint on the product of sizes of the associated loop iteration space.
• These polices allow having a larger number of iterates than threads in the x, y, or z thread dimension.
• CUDA/HIP thread and block loop policies are recommended for most loop patterns.

Finally

Note

CUDA/HIP block-direct policies may be preferable to block-loop policies in situations where block load balancing may be an issue as the block-direct policies may yield better performance.

GPU Policies for SYCL

SYCL Execution Policies	Works with	Brief description
sycl_exec<WORK_GROUP_SIZE>	forall,	Execute loop iterations in a GPU kernel launched with given work group size.
sycl_global_0<WORK_GROUP_SIZE>	kernel (For)	Map loop iterates directly to GPU global ids in first dimension, one iterate per work item. Group execution into work groups of given size.
sycl_global_1<WORK_GROUP_SIZE>	kernel (For)	Same as above, but map to global ids in second dim
sycl_global_2<WORK_GROUP_SIZE>	kernel (For)	Same as above, but map to global ids in third dim
sycl_local_0_direct	kernel (For)	Map loop iterates directly to GPU work items in first dimension, one iterate per work item (see note below about limitations)
sycl_local_1_direct	kernel (For)	Same as above, but map to work items in second dim
sycl_local_2_direct	kernel (For)	Same as above, but map to work items in third dim
sycl_local_0_loop	kernel (For)	Similar to local-1-direct policy, but use a work group-stride loop which doesn’t limit number of loop iterates
sycl_local_1_loop	kernel (For)	Same as above, but for work items in second dimension
sycl_local_2_loop	kernel (For)	Same as above, but for work items in third dimension
sycl_group_0_direct	kernel (For)	Map loop iterates directly to GPU group ids in first dimension, one iterate per group
sycl_group_1_direct	kernel (For)	Same as above, but map to groups in second dimension
sycl_group_2_direct	kernel (For)	Same as above, but map to groups in third dimension
sycl_group_0_loop	kernel (For)	Similar to group-1-direct policy, but use a group-stride loop.
sycl_group_1_loop	kernel (For)	Same as above, but use groups in second dimension
sycl_group_2_loop	kernel (For)	Same as above, but use groups in third dimension

OpenMP Target Offload Policies

RAJA provides policies to use OpenMP to offload kernel execution to a GPU device, for example. They are summarized in the following table.

OpenMP Target Execution Policies	Works with	Brief description
omp_target_parallel_for_exec<#>	forall, kernel(For)	Create parallel target region and execute with given number of threads per team inside it. Number of teams is calculated internally; i.e., apply omp teams distribute parallel for num_teams(iteration space size/#) thread_limit(#) pragma
omp_target_parallel_collapse_exec	kernel (Collapse)	Similar to above, but collapse perfectly-nested loops, indicated in arguments to RAJA Collapse statement. Note: compiler determines number of thread teams and threads per team

RAJA IndexSet Execution Policies

When an IndexSet iteration space is used in RAJA by passing an IndexSet to a RAJA::forall method, for example, an index set execution policy is required. An index set execution policy is a two-level policy: an ‘outer’ policy for iterating over segments in the index set, and an ‘inner’ policy used to execute the iterations defined by each segment. An index set execution policy type has the form:

RAJA::ExecPolicy< segment_iteration_policy, segment_execution_policy >

In general, any policy that can be used with a RAJA::forall method can be used as the segment execution policy. The following policies are available to use for the outer segment iteration policy:

Execution Policy	Brief description
Serial
seq_segit	Iterate over index set segments sequentially.
OpenMP CPU multithreading
omp_parallel_segit	Create OpenMP parallel region and iterate over segments in parallel inside it; i.e., apply omp parallel for pragma on loop over segments.
omp_parallel_for_segit	Same as above.
Intel Threading Building Blocks
tbb_segit	Iterate over index set segments in parallel using a TBB ‘parallel_for’ method.

Parallel Region Policies

Earlier, we discussed using the RAJA::region construct to execute multiple kernels in an OpenMP parallel region. To support source code portability, RAJA provides a sequential region concept that can be used to surround code that uses execution back-ends other than OpenMP. For example:

RAJA::region<RAJA::seq_region>([=]() {

   RAJA::forall<RAJA::loop_exec>(segment, [=] (int idx) {
       // do something at iterate 'idx'
   } );

   RAJA::forall<RAJA::loop_exec>(segment, [=] (int idx) {
       // do something else at iterate 'idx'
   } );

 });

Note

The sequential region specialization is essentially a pass through operation. It is provided so that if you want to turn off OpenMP in your code, for example, you can simply replace the region policy type and you do not have to change your algorithm source code.

Reduction Policies

Each RAJA reduction object must be defined with a ‘reduction policy’ type. Reduction policy types are distinct from loop execution policy types. It is important to note the following constraints about RAJA reduction usage:

Note

To guarantee correctness, a reduction policy must be consistent with the loop execution policy used. For example, a CUDA reduction policy must be used when the execution policy is a CUDA policy, an OpenMP reduction policy must be used when the execution policy is an OpenMP policy, and so on.

The following table summarizes RAJA reduction policy types:

Reduction Policy	Loop Policies to Use With	Brief description
seq_reduce	seq_exec, loop_exec	Non-parallel (sequential) reduction.
omp_reduce	any OpenMP policy	OpenMP parallel reduction.
omp_reduce_ordered	any OpenMP policy	OpenMP parallel reduction with result guaranteed to be reproducible.
omp_target_reduce	any OpenMP target policy	OpenMP parallel target offload reduction.
tbb_reduce	any TBB policy	TBB parallel reduction.
cuda/hip_reduce	any CUDA/HIP policy	Parallel reduction in a CUDA/HIP kernel (device synchronization will occur when reduction value is finalized).
cuda/hip_reduce_atomic	any CUDA/HIP policy	Same as above, but reduction may use CUDA atomic operations.
sycl_reduce	any SYCL policy	Reduction in a SYCL kernel (device synchronization will occur when the reduction value is finalized).

Note

RAJA reductions used with SIMD execution policies are not guaranteed to generate correct results. So they should not be used for kernels containing reductions.

Atomic Policies

Each RAJA atomic operation must be defined with an ‘atomic policy’ type. Atomic policy types are distinct from loop execution policy types.

Note

An atomic policy type must be consistent with the loop execution policy for the kernel in which the atomic operation is used. The following table summarizes RAJA atomic policies and usage.

Atomic Policy	Loop Policies to Use With	Brief description
seq_atomic	seq_exec, loop_exec	Atomic operation performed in a non-parallel (sequential) kernel.
omp_atomic	any OpenMP policy	Atomic operation in OpenM kernel.P multithreading or target kernel; i.e., apply omp atomic pragma.
cuda/hip/sycl_atomic	any CUDA/HIP/SYCL policy	Atomic operation performed in a CUDA/HIP/SYCL kernel.
cuda/hip_atomic_explicit	any CUDA/HIP policy	Atomic operation performed in a CUDA/HIP kernel that may also be used in a host execution context. The atomic policy takes a host atomic policy template argument. See additional explanation and example below.
builtin_atomic	seq_exec, loop_exec, any OpenMP policy	Compiler builtin atomic operation.
auto_atomic	seq_exec, loop_exec, any OpenMP policy, any CUDA/HIP/SYCL policy	Atomic operation compatible with loop execution policy. See example below. Can not be used inside cuda/hip explicit atomic policies.

Note

The cuda_atomic_explicit and hip_atomic_explicit policies take a host atomic policy template parameter. They are intended to be used with kernels that are host-device decorated to be used in either a host or device execution context.

Here is an example illustrating use of the cuda_atomic_explicit policy:

auto kernel = [=] RAJA_HOST_DEVICE (RAJA::Index_type i) {
  RAJA::atomicAdd< RAJA::cuda_atomic_explicit<omp_atomic> >(&sum, 1);
};

RAJA::forall< RAJA::cuda_exec<BLOCK_SIZE> >(RAJA::TypedRangeSegment<int> seg(0, N), kernel);

RAJA::forall< RAJA::omp_parallel_for_exec >(RAJA::TypedRangeSegment<int> seg(0, N), kernel);

In this case, the atomic operation knows when it is compiled for the device in a CUDA kernel context and the CUDA atomic operation is applied. Similarly when it is compiled for the host in an OpenMP kernel the omp_atomic policy is used and the OpenMP version of the atomic operation is applied.

Here is an example illustrating use of the auto_atomic policy:

RAJA::forall< RAJA::cuda_exec<BLOCK_SIZE> >(RAJA::TypedRangeSegment<int> seg(0, N),
  [=] RAJA_DEVICE (RAJA::Index_type i) {

  RAJA::atomicAdd< RAJA::auto_atomic >(&sum, 1);

});

In this case, the atomic operation knows that it is used in a CUDA kernel context and the CUDA atomic operation is applied. Similarly, if an OpenMP execution policy was used, the OpenMP version of the atomic operation would be used.

Note

• There are no RAJA atomic policies for TBB (Intel Threading Building Blocks) execution contexts since reductions are not supported for the RAJA TBB back-end.
• The builtin_atomic policy may be preferable to the omp_atomic policy in terms of performance.

Local Array Memory Policies

RAJA::LocalArray types must use a memory policy indicating where the memory for the local array will live. These policies are described in Local Array.

The following memory policies are available to specify memory allocation for RAJA::LocalArray objects:

• RAJA::cpu_tile_mem - Allocate CPU memory on the stack
• RAJA::cuda/hip_shared_mem - Allocate CUDA or HIP shared memory
• RAJA::cuda/hip_thread_mem - Allocate CUDA or HIP thread private memory

RAJA Kernel Execution Policies

RAJA kernel execution policy constructs form a simple domain specific language for composing and transforming complex loops that relies solely on standard C++14 template support. RAJA kernel policies are constructed using a combination of Statements and Statement Lists. A RAJA Statement is an action, such as execute a loop, invoke a lambda, set a thread barrier, etc. A StatementList is an ordered list of Statements that are composed in the order that they appear in the kernel policy to construct a kernel. A Statement may contain an enclosed StatmentList. Thus, a RAJA::KernelPolicy type is really just a StatementList.

The main Statement types provided by RAJA are RAJA::statement::For and RAJA::statement::Lambda, that we discussed in Complex Loops (RAJA::kernel). A RAJA::statement::For<ArgID, ExecPolicy, Enclosed Satements> type indicates a for-loop structure. The ArgID parameter is an integral constant that identifies the position of the iteration space in the iteration space tuple passed to the RAJA::kernel method to be used for the loop. The ExecPolicy is the RAJA execution policy to use on the loop, which is similar to RAJA::forall usage. The EnclosedStatements type is a nested template parameter that contains whatever is needed to execute the kernel and which forms a valid StatementList. The RAJA::statement::Lambda<LambdaID> type invokes the lambda expression corresponding to its position ‘LambdaID’ in the sequence of lambda expressions in the RAJA::kernel argument list. For example, a simple sequential for-loop:

for (int i = 0; i < N; ++i) {
  // loop body
}

can be represented using the RAJA kernel interface as:

using KERNEL_POLICY =
  RAJA::KernelPolicy<
    RAJA::statement::For<0, RAJA::seq_exec,
      RAJA::statement::Lambda<0>
    >
  >;

RAJA::kernel<KERNEL_POLICY>(
  RAJA::make_tuple(range),
  [=](int i) {
    // loop body
  }
);

Note

All RAJA::forall functionality can be done using the RAJA::kernel interface. We maintain the RAJA::forall interface since it is less verbose and thus more convenient for users.

RAJA::kernel Statement Types

The list below summarizes the current collection of statement types that can be used with RAJA::kernel and RAJA::kernel_param. More detailed explanation along with examples of how they are used can be found in the RAJA::kernel examples in RAJA Tutorial and Examples.

Note

All of the statement types described below are in the namespace RAJA::statement. For brevity, we omit the namespaces in the discussion in this section.

Note

RAJA::kernel_param functions similarly to RAJA::kernel except that the second argument is a tuple of parameters used in a kernel for local arrays, thread local variables, tiling information, etc.

Several RAJA statements can be specialized with auxilliary types, which are described in Auxilliary Types.

The following list contains the most commonly used statement types.

• For< ArgId, ExecPolicy, EnclosedStatements > abstracts a for-loop associated with kernel iteration space at tuple index ArgId, to be run with ExecPolicy execution policy, and containing the EnclosedStatements which are executed for each loop iteration.
• Lambda< LambdaId > invokes the lambda expression that appears at position ‘LambdaId’ in the sequence of lambda arguments. With this statement, the lambda expression must accept all arguments associated with the tuple of iteration space segments and tuple of parameters (if kernel_param is used).
• Lambda< LambdaId, Args...> extends the Lambda statement. The second template parameter indicates which arguments (e.g., which segment iteration variables) are passed to the lambda expression.
• Collapse< ExecPolicy, ArgList<...>, EnclosedStatements > collapses multiple perfectly nested loops specified by tuple iteration space indices in ArgList, using the ExecPolicy execution policy, and places EnclosedStatements inside the collapsed loops which are executed for each iteration. Note that this only works for CPU execution policies (e.g., sequential, OpenMP). It may be available for CUDA in the future if such use cases arise.

There is one statement specific to OpenMP kernels.

• OmpSyncThreads applies the OpenMP #pragma omp barrier directive.

Statement types that launch CUDA or HIP GPU kernels are listed next. They work similarly for each back-end and their names are distinguished by the prefix Cuda or Hip. For example, CudaKernel or HipKernel.

• Cuda/HipKernel< EnclosedStatements> launches EnclosedStatements as a GPU kernel; e.g., a loop nest where the iteration spaces of each loop level are associated with threads and/or thread blocks as described by the execution policies applied to them. This kernel launch is synchronous.
• Cuda/HipKernelAsync< EnclosedStatements> asynchronous version of Cuda/HipKernel.
• Cuda/HipKernelFixed<num_threads, EnclosedStatements> similar to Cuda/HipKernel but enables a fixed number of threads (specified by num_threads). This kernel launch is synchronous.
• Cuda/HipKernelFixedAsync<num_threads, EnclosedStatements> asynchronous version of Cuda/HipKernelFixed.
• CudaKernelFixedSM<num_threads, min_blocks_per_sm, EnclosedStatements> similar to CudaKernelFixed but enables a minimum number of blocks per sm (specified by min_blocks_per_sm), this can help increase occupancy. This kernel launch is synchronous. Note: there is no HIP variant of this statement.
• CudaKernelFixedSMAsync<num_threads, min_blocks_per_sm, EnclosedStatements> asynchronous version of CudaKernelFixedSM. Note: there is no HIP variant of this statement.
• Cuda/HipKernelOcc<EnclosedStatements> similar to CudaKernel but uses the CUDA occupancy calculator to determine the optimal number of threads/blocks. Statement is intended for use with RAJA::cuda/hip_block_{xyz}_loop policies. This kernel launch is synchronous.
• Cuda/HipKernelOccAsync<EnclosedStatements> asynchronous version of Cuda/HipKernelOcc.
• Cuda/HipKernelExp<num_blocks, num_threads, EnclosedStatements> similar to CudaKernelOcc but with the flexibility to fix the number of threads and/or blocks and let the CUDA occupancy calculator determine the unspecified values. This kernel launch is synchronous.
• Cuda/HipKernelExpAsync<num_blocks, num_threads, EnclosedStatements> asynchronous version of Cuda/HipKernelExp.
• Cuda/HipSyncThreads invokes CUDA or HIP __syncthreads() barrier.
• Cuda/HipSyncWarp invokes CUDA __syncwarp() barrier. Warp sync is not supported in HIP, so the HIP variant is a no-op.

Statement types that launch SYCL kernels are listed next.

• SyclKernel<EnclosedStatements> launches EnclosedStatements as a SYCL kernel. This kernel launch is synchronous.
• SyclKernelAsync<EnclosedStatements> asynchronous version of SyclKernel.

RAJA provides statements to define loop tiling which can improve performance; e.g., by allowing CPU cache blocking or use of GPU shared memory.

• Tile< ArgId, TilePolicy, ExecPolicy, EnclosedStatements > abstracts an outer tiling loop containing an inner for-loop over each tile. The ArgId indicates which entry in the iteration space tuple to which the tiling loop applies and the TilePolicy specifies the tiling pattern to use, including its dimension. The ExecPolicy and EnclosedStatements are similar to what they represent in a statement::For type.
• TileTCount< ArgId, ParamId, TilePolicy, ExecPolicy, EnclosedStatements > abstracts an outer tiling loop containing an inner for-loop over each tile, where it is necessary to obtain the tile number in each tile. The ArgId indicates which entry in the iteration space tuple to which the loop applies and the ParamId indicates the position of the tile number in the parameter tuple. The TilePolicy specifies the tiling pattern to use, including its dimension. The ExecPolicy and EnclosedStatements are similar to what they represent in a statement::For type.
• ForICount< ArgId, ParamId, ExecPolicy, EnclosedStatements > abstracts an inner for-loop within an outer tiling loop where it is necessary to obtain the local iteration index in each tile. The ArgId indicates which entry in the iteration space tuple to which the loop applies and the ParamId indicates the position of the tile index parameter in the parameter tuple. The ExecPolicy and EnclosedStatements are similar to what they represent in a statement::For type.

It is often advantageous to use local arrays for data accessed in tiled loops. RAJA provides a statement for allocating data in a Local Array object according to a memory policy. See Local Array Memory Policies for more information about such policies.

• InitLocalMem< MemPolicy, ParamList<...>, EnclosedStatements > allocates memory for a RAJA::LocalArray object used in kernel. The ParamList entries indicate which local array objects in a tuple will be initialized. The EnclosedStatements contain the code in which the local array will be accessed; e.g., initialization operations.

RAJA provides some statement types that apply in specific kernel scenarios.

• Reduce< ReducePolicy, Operator, ParamId, EnclosedStatements > reduces a value across threads in a multithreaded code region to a single thread. The ReducePolicy is similar to what it represents for RAJA reduction types. ParamId specifies the position of the reduction value in the parameter tuple passed to the RAJA::kernel_param method. Operator is the binary operator used in the reduction; typically, this will be one of the operators that can be used with RAJA scans (see RAJA Scan Operators). After the reduction is complete, the EnclosedStatements execute on the thread that received the final reduced value.
• If< Conditional > chooses which portions of a policy to run based on run-time evaluation of conditional statement; e.g., true or false, equal to some value, etc.
• Hyperplane< ArgId, HpExecPolicy, ArgList<...>, ExecPolicy, EnclosedStatements > provides a hyperplane (or wavefront) iteration pattern over multiple indices. A hyperplane is a set of multi-dimensional index values: i0, i1, … such that h = i0 + i1 + … for a given h. Here, ArgId is the position of the loop argument we will iterate on (defines the order of hyperplanes), HpExecPolicy is the execution policy used to iterate over the iteration space specified by ArgId (often sequential), ArgList is a list of other indices that along with ArgId define a hyperplane, and ExecPolicy is the execution policy that applies to the loops in ArgList. Then, for each iteration, everything in the EnclosedStatements is executed.

Auxilliary Types

The following list summarizes auxilliary types used in the above statements. These types live in the RAJA namespace.

• tile_fixed<TileSize> tile policy argument to a Tile or TileTCount statement; partitions loop iterations into tiles of a fixed size specified by TileSize. This statement type can be used as the TilePolicy template parameter in the Tile statements above.
• tile_dynamic<ParamIdx> TilePolicy argument to a Tile or TileTCount statement; partitions loop iterations into tiles of a size specified by a TileSize{} positional parameter argument. This statement type can be used as the TilePolicy template paramter in the Tile statements above.
• Segs<...> argument to a Lambda statement; used to specify which segments in a tuple will be used as lambda arguments.
• Offsets<...> argument to a Lambda statement; used to specify which segment offsets in a tuple will be used as lambda arguments.
• Params<...> argument to a Lambda statement; used to specify which params in a tuple will be used as lambda arguments.
• ValuesT<T, ...> argument to a Lambda statement; used to specify compile time constants, of type T, that will be used as lambda arguments.

Examples that show how to use a variety of these statement types can be found in Complex Loops (RAJA::kernel).