RAJA Tutorial and Examples

The following sections contain tutorial material and examples that describe how to use RAJA features.

RAJA Tutorial

This section contains a self-paced tutorial that shows how to use many RAJA features by way of a sequence of examples and exercises. Each exercise is located in files in the RAJA/exercises directory, one exercise file with code sections removed and comments containing instructions to fill in the missing code parts and one solution file containing complete working code to compare with and for guidance if you get stuck working on the exercise file. You are encouraged to build and run the exercises and modify them to try out different variations.

We also maintain a repository of tutorial slide presentations RAJA Tutorials Repo which we use when we give in-person or virtual online tutorials in various venues. The presentations complement the material found here. The tutorial material evolves as we add new features to RAJA, so refer to it periodically if you are interested in learning about new things in RAJA.

To understand the GPU examples (e.g., CUDA), it is also important to know the difference between CPU (host) and GPU (device) memory allocations and how transfers between those memory spaces work. For a detailed discussion, see Device Memory.

It is important to note that RAJA does not provide a memory model. This is by design as application developers who use RAJA prefer to manage memory in different ways. Thus, users are responsible for ensuring that data is properly allocated and initialized on a GPU device when running GPU code. This can be done using explicit host and device allocation and copying between host and device memory spaces or via unified memory (UM), if available. The RAJA Portability Suite contains other libraries, namely CHAI and Umpire, that complement RAJA by providing alternatives to manual programming model specific memory operations.


Most of the CUDA GPU exercises use unified memory (UM) via a simple memory manager capability provided in a file in the RAJA/exercises directory. HIP GPU exercises use explicit host and device memory allocations and explicit memory copy operations to move data between the two.

A Little C++ Background

To understand the discussion and code examples, a working knowledge of C++ templates and lambda expressions is required. So, before we begin, we provide a bit of background discussion of basic aspects of how RAJA use employs C++ templates and lambda expressions, which is essential to use RAJA successfully.

RAJA is almost an entirely header-only library that makes heavy use of C++ templates. Using RAJA most easily and effectively is done by representing the bodies of loop kernels as C++ lambda expressions. Alternatively, C++ functors can be used, but they make application source code more complex, potentially placing a significant negative burden on source code readability and maintainability.

C++ Templates

C++ templates enable one to write type-generic code and have the compiler generate an implementation for each set of template parameter types specified. For example, the RAJA::forall method to execute loop kernels is essentially method defined as:

template <typename ExecPol,
          typename IdxType,
          typename LoopBody>
forall(IdxType&& idx, LoopBody&& body) {

Here, “ExecPol”, “IdxType”, and “LoopBody” are C++ types that a user specifies in her code and which are seen by the compiler when the code is built. For example:

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

is a sequential CPU RAJA kernel that performs an element-by-element vector sum. The C-style analogue of this kernel is:

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

The execution policy type RAJA::loop_exec template argument is used to choose as specific implementation of the RAJA::forall method. The IdxType and LoopBody types are deduced by the compiler based the arguments passed to the RAJA::forall method; i.e., the IdxType is the stride-1 index range:

RAJA::TypedRangeSegment<int>(0, N)

and the LoopBody type is the lambda expression:

[=](int i) { a[i] = b[i] + c[i]; }

Elements of C++ Lambda Expressions

Here, we provide a brief description of the basic elements of C++ lambda expressions. A more technical and detailed discussion is available here: Lambda Functions in C++11 - the Definitive Guide

Lambda expressions were introduced in C++ 11 to provide a lexical-scoped name binding; specifically, a closure that stores a function with a data environment. That is, a lambda expression can capture variables from an enclosing scope for use within the local scope of the function expression.

A C++ lambda expression has the following form:

[capture list] (parameter list) {function body}

The capture list specifies how variables outside the lambda scope are pulled into the lambda data environment. The parameter list defines arguments passed to the lambda function body – for the most part, lambda arguments are just like arguments in a regular C++ method. Variables in the capture list are initialized when the lambda expression is created, while those in the parameter list are set when the lambda expression is called. The body of a lambda expression is similar to the body of an ordinary C++ method. RAJA kernel execution templates, such as RAJA::forall and RAJA::kernel that we will describe in detail later, pass arguments to lambdas based on usage and context such as loop iteration indices.

A C++ lambda expression can capture variables in the capture list by value or by reference. This is similar to how arguments to C++ methods are passed; i.e., pass-by-reference or pass-by-value. However, there are some subtle differences between lambda variable capture rules and those for ordinary methods. Variables included in the capture list with no extra symbols are captured by value. Variables captured by value are effectively const inside the lambda expression body and cannot be written to. Capture-by-reference is accomplished by using the reference symbol ‘&’ before the variable name similar to C++ method arguments. For example:

int x;
int y = 100;
[&x, &y](){ x = y; };

generates a lambda expression that captures both ‘x’ and ‘y’ by reference and assigns the value of ‘y’ to ‘x’ when called. The same outcome would be achieved by writing:

[&](){ x = y; };   // capture all lambda arguments by reference...


[=, &x](){ x = y; };  // capture 'x' by reference and 'y' by value...

Note that the following two attempts will generate compilation errors:

[=](){ x = y; };      // error: all lambda arguments captured by value,
                      //        so cannot assign to 'x'.
[x, &y](){ x = y; };  // error: cannot assign to 'x' since it is captured
                      //        by value.


A variable that is captured by value in a lambda expression is read-only.

A Few Notes About Lambda Usage With RAJA

There are several issues to note about using C++ lambda expressions to represent kernel bodies with RAJA. We describe them here.

  • Prefer by-value lambda capture.

    We recommend capture by-value for all lambda kernel bodies passed to RAJA execution methods. To execute a RAJA loop on a non-CPU device, such as a GPU, all variables accessed in the loop body must be passed into the GPU device data environment. Using capture by-value for all RAJA-based lambda usage will allow your code to be portable for either CPU or GPU execution. In addition, the read-only nature of variables captured by-value can help avoid incorrect CPU code since the compiler will report incorrect usage.

  • The ‘__device__’ annotation is required for device execution using CUDA or HIP.

    Any lambda passed to a CUDA or HIP execution context (or function called from a device kernel, for that matter) must be decorated with the __device__ annotation; for example:

    RAJA::forall<RAJA::cuda_exec<BLOCK_SIZE>>( range, [=] __device__ (int i) { ... } );

    Without this, the code will not compile and generate compiler errors indicating that a ‘host’ lambda cannot be called in ‘device’ code.

    RAJA provides the macro RAJA_DEVICE that can be used to help switch between host-only or device-only compilation.

  • Use ‘host-device’ annotation on a lambda carefully.

    RAJA provides the macro RAJA_HOST_DEVICE to support the dual annotation __ host__ __device__, which makes a lambda or function callable from CPU or GPU device code. However, when CPU performance is important, the host-device annotation should be applied carefully on a lambda that is used in a host (i.e., CPU) execution context. Although compiler improvements in recent years have significantly improved support for host-device lambda expressions, a loop kernel containing a lambda annotated in this way may run noticeably slower on a CPU than the same lambda with no annotation depending on the version of the compiler (e.g., nvcc) you are using. To be sure that your code does not suffer in performance, we recommend comparing CPU execution timings of important kernels with and without the __host__ __device__ annotation.

  • Cannot use ‘break’ and ‘continue’ statements in a lambda.

    In this regard, a lambda expression is similar to a function. So, if you have loops in your code with these statements, they should be rewritten.

  • Global variables are not captured in a lambda.

    This fact is due to the C++ standard. If you need access to a global variable inside a lambda expression, one solution is to make a local reference to it; for example:

    double& ref_to_global_val = global_val;
    RAJA::forall<RAJA::cuda_exec<BLOCK_SIZE>>( range, [=] __device__ (int i) {
      // use ref_to_global_val
    } );

  • Local stack arrays may not be captured by CUDA device lambdas.

    Although this is inconsistent with the C++ standard (local stack arrays are properly captured in lambdas for code that will execute on a CPU), attempting to access elements in a local stack array in a CUDA device lambda may generate a compilation error depending on the version of the device compiler you are using. One solution to this problem is to wrap the array in a struct; for example:

    struct array_wrapper {
      int[4] array;
    } bounds;
    bounds.array = { 0, 1, 8, 9 };
    RAJA::forall<RAJA::cuda_exec<BLOCK_SIZE>>(range, [=] __device__ (int i) {
      // access entries of bounds.array
    } );

    This issue was resolved in the 10.1 release of CUDA. If you are using an earlier version, an implementation similar to the one above will be required.

RAJA Examples and Exercises

The remainder of this tutorial illustrates how to use RAJA features with working code examples and interactive exercises. Files containing the exercise source code are located in the RAJA/exercises directory. Additional information about the RAJA features used can be found in RAJA Features.

The examples demonstrate CPU execution (sequential and OpenMP multithreading) and GPU execution (CUDA and/or HIP). Examples that show how to use RAJA with other parallel programming model back-ends will appear in future RAJA releases. For adventurous users who wish to try experimental RAJA back-end support, usage is similar to what is shown in the examples here.

All RAJA programming model support features are enabled via CMake options, which are described in Build Configuration Options.

Simple Loops and Basic RAJA Features

The examples in this section illustrate how to use RAJA::forall methods to execute simple loop kernels; i.e., non-nested loops. It also describes iteration spaces, reductions, atomic operations, scans, sorts, and RAJA data views.

Complex Loops and Advanced RAJA Features

RAJA provides two APIs for writing complex kernels involving nested loops: RAJA::kernel that has been available for several years and RAJA::expt::launch, which is more recent and which will be moved out of the expt namespace soon. We briefly introduce both interfaces here. The tutorial sections that follow provide much more detailed descriptions.

RAJA::kernel is analogous to RAJA::forall in that it involves kernel execution templates, execution policies, iteration spaces, and lambda expression kernel bodies. The main differences between RAJA::kernel and RAJA::forall are:

  • RAJA::kernel requires a tuple of iteration spaces, one for each level in a loop nest, whereas RAJA::forall takes exactly one iteration space.
  • RAJA::kernel can accept multiple lambda expressions to express different parts of a kernel body, whereas RAJA::forall accepts exactly one lambda expression for a kernel body.
  • RAJA::kernel execution policies are more complicated than those for RAJA::forall. RAJA::forall policies essentially represent the kernel execution back-end only. RAJA::kernel execution policies enable complex compile time algorithm transformations to be done without changing the kernel code.

The following exercises illustrate the common usage of RAJA::kernel and ``RAJA::expt::launch. Please see RAJA Kernel Execution Policies for more information about other execution policy constructs RAJA::kernel provides. RAJA::expt::launch takes a RAJA::expt::Grid type argument for representing a teams-thread launch configuration, and a lambda expression which takes a RAJA::expt::LaunchContext argument. RAJA::expt::launch allows an optional run time choice of execution environment, either CPU or GPU. Code written inside the lambda expression body will execute in the chosen execution environment. Within that environment, a user executes kernel operations using RAJA::expt::loop<EXEC_POL> method calls, which take lambda expressions to express loop body operations.


A key difference between the RAJA::kernel and RAJA::expt::launch approaches is that almost all of the kernel execution pattern is expressed in the execution policy when using RAJA::kernel, whereas with RAJA::expt::launch the kernel execution pattern is expressed mostly in the lambda expression kernel body.

One may argue that RAJA::kernel is more portable and flexible in that the execution policy enables compile time code transformations without changing kernel body code. On the other hand, RAJA::expt::launch is less opaque and more intuitive, but may require kernel body code changes for algorithm changes. Which interface to use depends on personal preference and other concerns, such as portability requirements, the need for run time execution selection, etc. Kernel structure is more explicit in application source code with RAJA::expt::launch, and more concise and arguably more opaque with RAJA::kernel. There is a large overlap of algorithms that can be expressed with either interface. However, there are things that one can do with one or the other but not both.

In the following sections, we introduce the basic mechanics and features of both APIs with examples and exercises. We also present a sequence of execution policy examples and matrix transpose examples using both RAJA::kernel and RAJA::expt::launch to compare and contrast the two interfaces.

Nested Loops with RAJA::kernel

The examples in this section illustrate various features of the RAJA::kernel API used to execute nested loop kernels. It describes how to construct kernel execution policies and use different view types and tiling mechanisms to transform loop patterns. More information can be found in Complex Loops (RAJA::kernel).

Nested Loops with RAJA::expt::launch

The examples in this section illustrate how to use RAJA::expt::launch to create an run time selectable execution space for expressing algorithms as nested loops.

Comparing RAJA::kernel and RAJA::expt::launch: Matrix-Transpose

In this section, we compare RAJA::kernel and RAJA::expt::launch implementations of a matrix transpose algorithm. We illustrate implementation differences of the two interfaces as we build upon each example with more complex features.