Elements of Loop Execution

In this section, we describe the basic elements of RAJA loop kernel execution. RAJA::forall, RAJA::kernel, and RAJA::expt::launch (aka RAJA Teams) template methods comprise the RAJA interface for loop execution. RAJA::forall methods execute simple, non-nested loops, RAJA::kernel methods support nested loops and other complex loop kernels and transformations, and RAJA::expt::launch creates an execution space in which algorithms are expressed in terms of nested loops using the RAJA::expt::loop method.

Note

• The forall , and kernel methods are in the namespace RAJA, while launch is found under the RAJA namespace for experimental features RAJA::expt.

• A RAJA::forall loop execution method is a template on an execution policy type. A RAJA::forall method takes two arguments:

  • an iteration space object, such as a contiguous range of loop indices, and
  • a single lambda expression representing the loop body.

• Each RAJA::kernel method is a template on a policy that contains statements with execution policy types appropriate for the kernel structure; e.g., an execution policy for each level in a loop nest. A RAJA::kernel method takes multiple arguments:

  • a tuple of iteration space objects, and
  • one or more lambda expressions representing portions of the loop kernel body.

• The RAJA::expt::launch method is a template on both host and device policies to create an execution space for kernels. Since both host and device poilices are specified, the launch method can be used to select at run-time whether to run a kernel on the host or device. Algorithms are expressed inside the execution space as nested loops using RAJA::loop methods.

  • Hierarchical parallelism can be expressed using the thread and thread-team model with RAJA::expt::loop methods as found in programming models such as CUDA/HIP.

Various examples showing how to use RAJA::forall, RAJA::kernel, RAJA::launch methods may be found in the RAJA Tutorial.

For more information on RAJA execution policies and iteration space constructs, see Policies and Indices, Segments, and IndexSets, respectively.

Simple Loops (RAJA::forall)

As noted earlier, a RAJA::forall template executes simple (i.e., non-nested) loops. For example, a C-style loop that adds two vectors, like this:

for (int i = 0; i < N; ++i) {
  c[i] = a[i] + b[i];
}

may be written using RAJA as:

RAJA::forall<exec_policy>(RAJA::RangeSegment(0, N), [=] (int i) {
  c[i] = a[i] + b[i];
});

A RAJA::forall method is a template on an execution policy type and takes two arguments: an object describing the loop iteration space, such as a RAJA range segment (shown here), and a lambda expression for the loop body. Applying different loop execution policies enables the loop to run in different ways; e.g., using different programming model back-ends. Different iteration space objects enable the loop iterates to be partitioned, reordered, run in different threads, etc.

Note

Changing loop execution policy types and iteration space constructs enables loops to run in different ways by recompiling the code and without modifying the loop kernel code.

While loop execution using RAJA::forall methods is a subset of RAJA::kernel functionality, described next, we maintain the RAJA::forall interface for simple loop execution because the syntax is simpler and less verbose for that use case.

Note

Data arrays in lambda expressions used with RAJA are typically RAJA Views (see View and Layout) or bare pointers as shown in the code snippets above. Using something like ‘std::vector’ is non-portable (won’t work in GPU kernels, generally) and would add excessive overhead for copying data into the lambda data environment when captured by value.

Complex Loops (RAJA::kernel)

A RAJA::kernel template provides ways to compose and execute arbitrary loop nests and other complex kernels. To introduce the RAJA kernel interface, consider a (N+1)-level C-style loop nest:

for (int iN = 0; iN < NN; ++iN) {
  ...
     for (int i0 = 0; i0 < N0; ++i0) {s
       \\ inner loop body
     }
}

Note that we could write this by nesting RAJA::forall statements and it would work for some execution policy choices:

RAJA::forall<exec_policyN>(IN, [=] (int iN) {
  ...
     RAJA::forall<exec_policy0>(I0, [=] (int i0)) {
       \\ inner loop body
     }
  ...
}

However, this approach treats each loop level as an independent entity. This makes it difficult to parallelize the levels in the loop nest together. So it may limit the amount of parallelism that can be exposed and the types of parallelism that may be used. For example, if an OpenMP or CUDA parallel execution policy is used on the outermost loop, then all inner loops would be run sequentially in each thread. It also makes it difficult to perform transformations like loop interchange and loop collapse without changing the source code, which breaks RAJA encapsulation.

Note

We do not recommend nesting ``RAJA::forall`` statements.

The RAJA kernel interface facilitates parallel execution and compile-time transformation of arbitrary loop nests and other complex loop structures. It can treat a complex loop structure as a single entity, which simplifies the ability to transform and apply different parallel execution patterns by changing the execution policy type and not the kernel code.

The loop above nest may be written using the RAJA kernel interface as:

using KERNEL_POL =
  RAJA::KernelPolicy< RAJA::statement::For<N, exec_policyN,
                        ...
                          RAJA::statement::For<0, exec_policy0,
                            RAJA::statement::Lambda<0>
                          >
                        ...
                      >
                    >;

RAJA::kernel< KERNEL_POL >(
  RAJA::make_tuple(RAJA::RangeSegment(0, NN), ..., RAJA::RangeSegment(0, N0),

  [=] (int iN, ... , int i0) {
     // inner loop body
  }

);

A RAJA::kernel method takes a RAJA::KernelPolicy type template parameter, and a tuple of iteration spaces and a sequence of lambda expressions as arguments.

In the case we discuss here, the execution policy contains a nested sequence of RAJA::statement::For statements, one for each level in the loop nest. Each For statement takes three template parameters:

• an integral index parameter that binds the For statement to the item in the iteration space tuple corresponding to that index,
• an execution policy type for the associated loop nest level, and
• an enclosed statement list (described in RAJA Kernel Execution Policies).

Note

The nesting of RAJA::statement::For types is analogous to the nesting of for-statements in the C-style version of the loop nest. One can think of the ‘<, >’ symbols enclosing the template parameter lists as being similar to the curly braces in C-style code.

Here, the innermost type in the kernel policy is a RAJA::statement::Lambda<0> type indicating that the first lambda expression (argument zero of the sequence of lambdas passed to the RAJA::kernel method) will comprise the inner loop body. We only have one lambda in this example but, in general, we can have any number of lambdas and we can use any subset of them, with RAJA::statement::Lambda types placed appropriately in the execution policy, to construct a loop kernel. For example, placing RAJA::statement::Lambda types between RAJA::statement::For statements enables non-perfectly nested loops.

RAJA offers two types of lambda statements. The first as illustratated above, requires that each lambda expression passed to a RAJA::kernel method must take an index argument for each iteration space in the tuple. With this type of lambda statement, the entire iteration space must be active in a containing For construct. A compile time static_assert will be triggered if any of the arguments are undefined, indicating that something is not correct.

The second type of lambda statement, an extension of the first, takes additional template parameters which specify which iteration space indices are passed as lambda arguments. The result is that a kernel lambda only needs to accept iteration space index arguments that are used in the lambda body.

The kernel policy list with lambda arguments may be written as:

using KERNEL_POL =
  RAJA::KernelPolicy< RAJA::statement::For<N, exec_policyN,
                        ...
                          RAJA::statement::For<0, exec_policy0,
                            RAJA::statement::Lambda<0, RAJA::Segs<N,...,0>>
                          >
                        ...
                      >
                    >;

The template parameter RAJA::Segs is used to specify which elements in the segment tuple are used to pass arguments to a lambda. RAJA offers other types such as RAJA::Offsets, and RAJA::Params to identify offsets and parameters in segments and param tuples respectively to be used as lambda argumentsx. See Matrix Multiplication (Nested Loops) and Matrix Transpose with Local Array for detailed examples.

Note

Unless lambda arguments are specified in RAJA lambda statements, the loop index arguments for each lambda expression used in a RAJA kernel loop body must match the contents of the iteration space tuple in number, order, and type. Not all index arguments must be used in a lambda, but they all must appear in the lambda argument list and all must be in active loops to be well-formed. In particular, your code will not compile if this is not done correctly. If an argument is unused in a lambda expression, you may include its type and omit its name in the argument list to avoid compiler warnings just as one would do for a regular C++ method with unused arguments.

For RAJA nested loops implemented with RAJA::kernel, as shown here, the loop nest ordering is determined by the order of the nested policies, starting with the outermost loop and ending with the innermost loop.

Note

The integer value that appears as the first parameter in each RAJA::statement::For template indicates which iteration space tuple entry or lambda index argument it corresponds to. This allows loop nesting order to be changed simply by changing the ordering of the nested policy statements. This is analogous to changing the order of ‘for-loop’ statements in C-style nested loop code.

See Basic RAJA::kernel Variants for a complete example showing RAJA nested loop functionality and Nested Loop Interchange for a detailed example describing nested loop reordering.

Note

In general, RAJA execution policies for RAJA::forall and RAJA::kernel are different. A summary of all RAJA execution policies that may be used with RAJA::forall or RAJA::kernel may be found in Policies.

Finally, a discussion of how to construct RAJA::KernelPolicy types and available RAJA::statement types can be found in RAJA Kernel Execution Policies.

Team based loops (RAJA::launch)

The RAJA Teams framework aims to unify thread/block based programming models such as CUDA/HIP/SYCL while maintaining portability on host backends (OpenMP, sequential). When using the RAJA::kernel interface, developers express all aspects of nested loop execution in the execution policy type on which the RAJA::kernel method is templated. In contrast, the RAJA::launch interface allows users to express nested loop execution in a manner that more closely reflects how one would write conventional nested C-style for-loop code. Additionally, RAJA Teams introduces run-time host or device selectable kernel execution. The main application of RAJA Teams is imperfectly nested loops. Using the RAJA::expt::launch method developers are provided with an execution space enabling them to express algorithms in terms of nested RAJA::expt::loop statements:

RAJA::expt::launch<launch_policy>(select_CPU_or_GPU)
RAJA::expt::Grid(RAJA::expt::Teams(NE), RAJA::expt::Threads(Q1D)),
[=] RAJA_HOST_DEVICE (RAJA::expt::Launch ctx) {

  RAJA::expt::loop<team_x> (ctx, RAJA::RangeSegment(0, teamRange), [&] (int bx) {

    RAJA_TEAM_SHARED double s_A[SHARE_MEM_SIZE];

    RAJA::expt::loop<thread_x> (ctx, RAJA::RangeSegment(0, threadRange), [&] (int tx) {
      s_A[tx] = tx;
    });

      ctx.teamSync();

 )};

});

The underlying idea of RAJA Teams is to enable developers to express nested parallelism in terms of teams and threads. Similar to the CUDA programming model, development is done using a collection of threads, threads are grouped into teams. Using the RAJA::expt::loop methods iterations of the loop may be executed by threads or teams (depending on the execution policy). The launch context serves to synchronize threads within the same team. The RAJA Teams abstraction consist of three main concepts.

• Launch Method: creates an execution space in which developers may express their algorithm in terms of nested RAJA::expt::loop statements. The loops are then executed by threads or thread-teams. The method is templated on both a host and device execution space and enables run-time selection of the execution environment.
• Resources: holds a number of teams and threads (akin to CUDA blocks/threads).
• Loops: are used to express hierarchical parallelism. Work within a loop is mapped to either teams or threads. Team shared memory is available by using the RAJA_TEAM_SHARED macro. Team shared memory enables threads in a given team to share data. In practice, team policies are typically aliases for RAJA GPU block policies in the x,y,z dimensions (for example cuda_block_direct), while thread policies are aliases for RAJA GPU thread policies (for example cuda_thread_direct) x,y,z dimensions. On the host, teams and threads may be mapped to sequential loop execution or OpenMP threaded regions.

The team loop interface combines concepts from RAJA::forall and RAJA::kernel. Various policies from RAJA::kernel are compatible with the RAJA Teams framework.

MultiDimensional loops using Simple loop APIs (RAJA::CombiningAdapter)

A RAJA::CombiningAdapter object provides ways to run perfectly nested loops with simple loop APIs like RAJA::forall and RAJA::WorkGroup WorkGroup. To introduce the RAJA ::CombiningAdapter interface, consider a (N+1)-level C-style loop nest:

for (int iN = 0; iN < NN; ++iN) {
  ...
     for (int i0 = 0; i0 < N0; ++i0) {
       \\ inner loop body
     }
}

We can use a RAJA::CombiningAdapter to combine the iteration spaces of the loops and pass the adapter to a RAJA::forall statement to execute them:

auto adapter = RAJA::make_CombingingAdapter(
    [=] (int iN, ..., int i0)) {
      \\ inner loop body
    }, IN, ..., I0);

RAJA::forall<exec_policy>(adapter.getRange(), adapter);

A RAJA::CombiningAdapter object is a template combining a loop body and iteration spaces. The maker function template takes a lambda expression for the loop body and an arbitrary number of segment arguments. It provides a flattened index space via the getRange method that can be passed as the iteration space to the simple loop API. The object itself can be passed into the loop API as the loop body. The object’s call operator does the conversion of the flat single dimensional index into the multi-dimensional index space, calling the provided lambda with the appropriate indices.

Note

CombiningAdapter currently only supports RAJA::RangeSegment and RAJA::TypedRangeSegment segments.