RAJA Performance Suite

The RAJA Performance Suite is designed to explore performance of loop-based computational kernels found in HPC applications. Specifically, it can be used to assess and monitor runtime performance of kernels implemented using RAJA C++ performance portability abstractions and compare those to variants implemented using common parallel programming models, such as OpenMP and CUDA, directly. Some important terminology used in the Suite includes:

Kernel is a distinct loop-based computation that appears in the Suite in multiple variants (or implementations), each of which performs the same computation.
Variant is an implementation or set of implementations of a kernel in the Suite that share the same approach/abstraction and programming model, such as baseline OpenMP, RAJA OpenMP, etc.
Tuning is a particular implementation of a variant of a kernel in the Suite, such as gpu block size 128, gpu block size 256, etc.
Group is a collection of kernels in the Suite that are grouped together because they originate from the same source, such as a specific benchmark suite.

Each kernel in the Suite appears in multiple RAJA and non-RAJA (i.e., baseline) variants using parallel programming models that RAJA supports. Some kernels have multiple tunings of a variant to explore some of the parametrization that the programming model supports. The kernels originate from various HPC benchmark suites and applications. For example, the "Stream" group contains kernels from the Babel Stream benchmark, the "Apps" group contains kernels extracted from real scientific computing applications, and so forth.

The suite can be run as a single process or with multiple processes when configured with MPI support. Running with MPI in the same configuration used by an hpc app allows the suite to gather performance data that is more relevant for that hpc app than performance data gathered running single process. For example running sequentially with one MPI rank per core vs running sequentially with a single process yields different performance results on most multi-core CPUs.

Building the Suite

To build the Suite, you must first obtain a copy of the source code by cloning the GitHub repository. For example,

> mkdir RAJA-PERFSUITE
> cd RAJA-PERFSUITE
> git clone --recursive https://github.com/llnl/RAJAPerf.git

The repository will reside in a RAJAPerf sub-directory in the directory into which is was cloned.

The Performance Suite has two Git submodules, RAJA and the CMake-based BLT build system. The --recursive option tells Git to clone the submodules as well as any submodules that they use. If you switch to a different branch in your working copy of the repository, you should update the submodules to make sure you have the right versions of them for the branch. For example,

> cd RAJAPerf
> git checkout <some branch name>
> git submodule update --recursive

Note that the --recursive option will update submodules within submodules, similar to usage with the git clone as described above.

RAJA and the Performance Suite are built together using the same CMake configuration. For convenience, we include scripts in the scripts directory that invoke corresponding configuration files (CMake cache files) in the RAJA submodule. For example, the scripts/lc-builds directory contains scripts that show how we build code for testing on platforms in the Lawrence Livermore Computing Center. Each build script creates a descriptively-named build space directory in the top-level Performance Suite directory and runs CMake with a configuration appropriate for the platform and compilers used. After CMake completes, enter the build directory and type make (or make -j <N> for a parallel build using N processor cores; if you omit the number of cores, the code will build in parallel using all available cores on the node you are running on) to compile the code. For example,

> ./scripts/blueos_nvcc_clang.sh 10.2.89 sm_70 10.0.1
> cd build_blueos_nvcc10.2.89-cm_70-clang10.0.1
> make -j

The build scripts and associated CMake host-config files in RAJA are useful sources of information for building the Suite on various platforms. For example, they show how to enable specific back-end kernel variants and compiler options we use for testing.

You can also create your own build directory and run CMake with your own options from there. For example, :

> mkdir my-build
> cd my-build
> cmake <cmake args> ../
> make -j

The provided configurations will only build the Performance Suite code by default; i.e., it will not build any RAJA test or example codes. If you want to build the RAJA tests, for example, to verify your build of RAJA is working properly, just pass the -DENABLE_TESTS=On option to CMake, either on the command line if you run CMake directly or edit the script you are running to do this. Then, when the build completes, you can type make test to run the RAJA tests.

Building with MPI

Some of the provided configurations will build the Performance Suite with MPI support enabled. For example,

> ./scripts/blueos_spectrum_nvcc_clang.sh rolling-release 10.2.89 sm_70 10.0.1
> cd build_lc_blueos-spectrumrolling-release-nvcc10.2.89-sm_70-clang10.0.1
> make -j

In general MPI support can be enabled by passing the -DENABLE_MPI=On option to CMake and providing a mpi compiler wrapper via the -DMPI_CXX_COMPILER=/path/to/mpic++ option to CMake in addition to other CMake options. For example,

> mkdir my-mpi-build
> cd my-mpi-build
> cmake -DENABLE_MPI=On -DMPI_CXX_COMPILER=/path/to/mpic++ <cmake args> ../
> make -j

Building with specific GPU block size tunings

Some of the provided configurations will build the Performance Suite with GPU support enabled. This will build with the default GPU block size tuning for each kernel. For example,

> ./scripts/blueos_nvcc_clang.sh 10.2.89 sm_70 10.0.1
> cd build_lc_blueos-nvcc10.2.89-sm_70-clang10.0.1
> make -j

Using a specific set of GPU block sizes is done by by passing the -DRAJA_PERFSUITE_GPU_BLOCKSIZES=<list,of,block,sizes> option to CMake via the -DMPI_CXX_COMPILER=/path/to/mpic++ option to CMake in addition to other CMake options. For example,

> mkdir my-gpu-build
> cd my-gpu-build
> cmake -DRAJA_PERFSUITE_GPU_BLOCKSIZES=64,128,256,512,1024 <cmake args> ../
> make -j

Running the Suite

The Suite is run by invoking the executable in the bin sub-directory in the build space directory. For example, if you provide no command line options,

> ./bin/raja-perf.exe

the entire Suite (all kernels and variants) will execute in their default configurations. How the Suite will run and some details about each kernel will appear on the screen before it is run. Kernel detail information will also appear in a run report file generated in your run directory after the Suite executes. You can pass the ''--dryrun'' option along with any other runtime options to see a summary of how the Suite will execute without actually running it.

The Suite can be run in a variety of ways via options passed to the executable. For example, you can run subsets of kernels and variants by specifying variants, groups, or individual kernels explicitly. Other configuration options to set problem sizes, number of times each kernel is run, etc. can also be specified. You build the code once and use scripts or other mechanisms to run the Suite in different ways for analyses you want to perform.

All options appear in a 'long form' with a double hyphen prefix (i.e., '--'). Some options are available in a one or two character 'short form' with a single hyphen prefix (i.e., '-') for convenience. To see available options along with a brief description of each, pass the --help or -h option:

> ./bin/raja-perf.exe --help

or

> ./bin/raja-perf.exe -h

Lastly, the program will generate a summary of provided input if it is given input that the code does not know how to parse. Ill-formed input will be noted in the summary output. Hopefully, this will make it easy for users to correct erroneous usage, such as mis-spelled option names.

Running with MPI

Running the Suite with MPI is as simple as running any other MPI application. For example,

> srun -n 2 ./bin/raja-perf.exe

the entire Suite (all kernels and variants) will execute in their default configurations on each of the 2 ranks. The kernel information output shows how each kernel is run on each rank. The total problem size across all MPI ranks can be calculated by multiplying the number of MPI ranks by the problem size in the kernel information. Timing is reported on rank 0 and is gathered by doing an MPI barrier, starting the timer, running the kernel repetitions, doing an MPI barrier, and then stopping the timer.

Important note

The OpenMP target offload variants of the kernels in the Suite are a work-in-progress since the RAJA OpenMP target offload back-end is also a work-in-progress. If you configure them to build, they can be run with the executable ./bin/raja-perf-omptarget.exe which is distinct from the one described above. At the time the OpenMP target offload variants were developed, it was not possible for them to co-exist in the same executable as the CUDA variants, for example. In the future, the build system may be reworked so that the OpenMP target variants can be run from the same executable as the other variants.

Generated output

When the Suite is run, several output files are generated that contain data describing the run. The file names start with the file prefix provided via a command line option in the output directory, also specified on the command line. If no such options are provided, files will be located in the current run directory and be named RAJAPerf-*, where '*' is a string indicating the contents of the file.

Currently, there are five files generated:

Timing -- execution time (sec.) of each loop kernel and variant run
Checksum -- checksum values for each loop kernel and variant run to ensure they are producing the same results (typically, checksum differences of ~1e-10 or less indicate that all kernel variants ran correctly).
Speedup -- runtime speedup of each loop kernel and variant with respect to a reference variant. The reference variant can be set with a command line option. If not specified, the first variant run will be used as the reference. The reference variant used will be noted in the file.
Figure of Merit (FOM) -- basic statistics about speedup of RAJA variant vs. baseline for each programming model run. Also, when a RAJA variant timing differs from the corresponding baseline variant timing by more than some tolerance, this will be noted in the file with OVER_TOL. By default the tolerance is 10%. This can be changed via a command line option.
Kernel -- Basic information about each kernel that is run, which is the same for each variant of the kernel that is run. See description of output information below.

All output files are text files. Other than the checksum file, all are in 'csv' format for easy processing by common tools and generating plots.

Kernel information definitions

Information about kernels that are run is located in the ''RAJAPerf-kernels.csv'' file. This information is for each process individually, so when running with MPI the total problem size aggregated across all ranks is the number of ranks times the problem size shown in the kernel information. Kernel information includes the following:

Kernel name -- Format is group name followed by kernel name, separated by an underscore.
Feature -- RAJA feature(s) exercised in RAJA variants of kernel.
Problem size -- Size of the problem represented by a kernel. Please see notes below for more information.
Reps -- Number of times a kernel runs in a single pass through the Suite.
Iterations/rep -- Sum of sizes of all parallel iteration spaces for all loops run in a single kernel execution (see 'problem size' above).
Kernels/rep -- total number of loop structures run (or GPU kernels launched) in each kernel repetition.
Bytes/rep -- Total number of bytes read from and written to memory for each repetition of kernel.
FLOPs/rep -- Total number of floating point operations executed for each repetition of kernel. Currently, we count arithmetic operations (+, -, *, /) and functions, such as exp, sin, code, etc. as on FLOP. We do not currently count operations like abs and comparisons (<, >, etc.) in the FLOP count. So these numbers are rough estimates. For actual FLOP counts, a performancce analysis tool should be used.

Notes about 'problem size'

Problem size is always output per process/MPI rank. To get the total problem size across all ranks when running with MPI multiply the problem size by the number of MPI ranks.
The Suite uses three notions of problem size for each kernel: 'default', 'target', and 'actual'. Default is the 'default' problem size defined for a kernel and the size that will be run if no runtime options are provided to run a different size. Target is the desired problem size to run based on default settings and alterations to that if input is provided to change the default. Actual is the problem size that is run based on how each kernel calculates this.
The concept of problem size is subjective and can be interpreted differently depending on the kernel structure and what one is trying to measure. For example, problem size could refer to the amount of data needed to be stored in memory to run the problem, or it could refer to the amount of parallel work that is possible, etc.
We employ the following, admittedly loose definition, which depends on the particular kernel structure. Of all the 'loop structures' (e.g., single loop, nested loops, etc.) that are run for a kernel (note that some kernels run multiple loops, possibly with different sizes or loop structures), problem size refers to the size of the data set required to generate the kernel result. The interpretation of this and the definition of problem size for each kernel in the suite is determined by the kernel developer and team discussion.

Here are a few examples to give a better sense of how we determine problem size for various kernels in the Suite.

Vector addition.

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

The problem size for this kernel is 'N', the loop length. Note that this happens to match the size of the vectors a, b, c and the total amount of parallel work in the kernel. This is common for simple, data parallel kernels.

Matrix-vector multiplication.

for (int r = 0; r < N_r; ++r) {
  b[r] = 0;
  for (int c = 0; c < N_c; ++c) {
    b[r] += A[r][c] + x[c];
  }
}

The problem size if N_r * N_c, the size of the matrix. Note that this matches the total size of the problem iteration space, but the total amount of parallel work is N_r, the number of rows in the matrix and the length of the vector b.

Matrix-matrix multiplication.

for (int i = 0; i < N_i; ++i) {
  for (int j = 0; j < N_j; ++j) {
    A[i][j] = 0;
    for (int k = 0; k < N_k; ++k) {
      A[i][j] += B[i][k] * C[k][j];
    }
  }
}

Here, we are multiplying matrix B (N_i x N_k) and matrix C (N_k x N_j) and storing the result in matrix A (N_i X N_j). Problem size could be chosen to be the maximum number of entries in matrix B or C. We choose the size of matrix A (N_i * N_j), which is more closely aligned with the number of independent operations (i.e., the amount of parallel work) in the kernels.

Adding kernels, variants, and Tunings

This section describes how to add new kernels, variants and/or tunings to the Suite. Group and feature modifications are not required unless a new group or exercised RAJA feature is added when a new kernel is introduced. The information in this section also provides insight into how the performance Suite operates.

It is essential that the appropriate targets are updated in the appropriate CMakeLists.txt files when files are added to the Suite so that they will be compiled.

Adding a kernel

Adding a new kernel to the Suite involves three main steps:

Add a unique kernel ID and a unique kernel name to the Suite.
If the kernel is part of a new kernel group or exercises a new RAJA feature, also add a unique group ID and name for the group. Similarly, if a new RAJA feature is exercised by a new kernel.
Implement a kernel class that contains all operations needed to run it, with source files organized as described below.

These steps are described in the following sections.

Add the kernel ID and name

Two key pieces of information identify a kernel: the group in which it resides and the name of the kernel itself. For concreteness, we describe how to add a kernel "FOO" that lives in the kernel group "Basic". The files RAJAPerfSuite.hpp and RAJAPerfSuite.cpp in the src/common directory define enumeration values and arrays of string names for the kernels, respectively.

First, add an enumeration value identifier for the kernel, that is unique among all kernels, in the enum 'KernelID' in the header file RAJAPerfSuite.hpp:

enum KernelID {
..
  Basic_FOO,
..
};

Note: the enumeration value for the kernel is the group name followed by the kernel name, separated by an underscore. It is important to follow this convention so that the kernel works properly with the Performance Suite machinery.

Second, add the kernel name to the array of strings KernelNames in the file RAJAPerfSuite.cpp:

static const std::string KernelNames [] =
{
..
  std::string("Basic_FOO"),
..
};

Note: the kernel string name is just a string version of the kernel ID. This convention must be followed so that the kernel works properly with the Performance Suite machinery. Also, the values in the KernelID enum and the strings in the KernelNames array must be kept consistent (i.e., same order and matching one-to-one). Within each kernel group, we keep the kernel names and IDs in alphabetical order to make the organization clear.

Add new group if needed

If a kernel is added as part of a new group of kernels in the Suite, a new value must be added to the GroupID enum in the header file RAJAPerfSuite.hpp and an associated group string name must be added to the GroupNames array of strings in the file RAJAPerfSuite.cpp. Again, the enumeration values and items in the string array must be kept consistent (same order and matching one-to-one).

Adding a new RAJA feature is similar.

Add the kernel class

Each kernel in the Suite is implemented in a class whose header and implementation files live in the directory named for the group in which the kernel lives. The kernel class is responsible for implementing all operations needed to manage data, execute, and record execution timing and checksum information for each variant and tuning of the kernel. To properly plug in to the Performance Suite framework, the kernel class must be a subclass of the KernelBase base class that defines the interface for kernels in the Suite.

Continuing with our example, we add a 'FOO' class header file FOO.hpp, and multiple implementation files described in the following sections:

FOO.cpp contains the methods to setup and teardown the memory for the 'FOO' kernel, and compute and record a checksum on the result after it executes. It also specifies kernel information in the kernel class constructor.
FOO-Seq.cpp contains sequential CPU variants and tunings of the kernel.
FOO-OMP.cpp contains OpenMP CPU multithreading variants and tunings of the kernel.
FOO-OMPTarget.cpp contains OpenMP target offload variants and tunings of the kernel.
FOO-Cuda.cpp contains CUDA GPU variants and tunings of the kernel.
FOO-Hip.cpp contains HIP GPU variants and tunings of the kernel.

All kernels in the Suite follow the same implementation pattern. Inspect the files for any kernel to understand the overall organization.

Note: if a new execution back-end variant is added that is not listed here, that variant should go in the file FOO-<backend-name>.cpp. Keeping the back-end variants in separate files helps to understand compiler optimizations when looking at generated assembly code, for example.

Kernel class header

Here is what a header file for the FOO kernel object should look like:

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~//
// Copyright (c) 2017-23, Lawrence Livermore National Security, LLC
// and RAJA Performance Suite project contributors.
// See the RAJAPerf/LICENSE file for details.
//
// SPDX-License-Identifier: (BSD-3-Clause)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~//

///
/// Foo kernel reference implementation:
///
/// Describe it here...
///

#ifndef RAJAPerf_Basic_FOO_HPP
#define RAJAPerf_Basic_FOO_HPP

#include "common/KernelBase.hpp"

namespace rajaperf
{
class RunParams; // Forward declaration for ctor arg.

namespace basic
{

class FOO : public KernelBase
{
public:

  FOO(const RunParams& params);

  ~FOO();

  void setUp(VariantID vid, size_t tuning_idx);
  void updateChecksum(VariantID vid, size_t tuning_idx);
  void tearDown(VariantID vid, size_t tuning_idx);

  void runSeqVariant(VariantID vid, size_t tuning_idx);
  void runOpenMPVariant(VariantID vid, size_t tuning_idx);
  void runCudaVariant(VariantID vid, size_t tuning_idx);
  void runHipVariant(VariantID vid, size_t tuning_idx);
  void runOpenMPTargetVariant(VariantID vid, size_t tuning_idx);

private:
  // Kernel-specific data (pointers, scalars, etc.) as needed...
};

} // end namespace basic
} // end namespace rajaperf

#endif // closing endif for header file include guard

The kernel object header has a uniquely-named header file include guard and the class is nested within the rajaperf and basic namespaces. The constructor takes a reference to a RunParams object, which contains the input parameters for running the Suite -- we'll say more about this later. The methods that take a variant ID and tuning index arguments must be provided as they are pure virtual in the KernelBase class. Their names are descriptive of what they do and we'll provide more details about them when we describe the class implementation next.

Kernel class implementation

Each kernel in the Suite follows a similar implementation pattern for consistency and ease of analysis and understanding. Here, we describe several key steps and conventions that must be followed to ensure that all kernels interact with the performance Suite machinery in the same way:

Initialize the KernelBase class object with KernelID and RunParams object passed to the FOO class constructor.
In the class constructor, define kernel information. This includes: default problem size, default run repetition count, iterations per rep, kernels per rep, bytes per rep, FLOPs per rep, the RAJA features used by the kernel, and kernel variants defined (i.e., implemented) by calling the appropriate members in the KernelBase class. See the *.cpp file for any existing kernel in the suite for examples of how this is done. Note that tuning names are added in step 6.
Implement data allocation and initialization operations for each kernel variant in the setUp method.
Compute the checksum for each variant in the updateChecksum method.
Deallocate and reset any data that will be allocated and/or initialized in subsequent kernel executions in the tearDown method.
Implement kernel execution for the associated variants and tunings in the run*Variant methods in the proper source files. Add tuning names for the tunings of each variant by overriding the KernelBase methods set*TuningDefinitions. Note that this is not necessary if there is only one tuning.

Constructor and destructor

It is important to note that there will only be one instance of each kernel class created by the program. Thus, each kernel class constructor and destructor must only perform operations that are not specific to any kernel variant.

The constructor must pass the kernel ID and RunParams object to the base class KernelBase constructor. The body of the constructor must also call base class methods to set kernel information described above. Note that the arguments passed to each method are specific to each kernel, in general. This code snippets shows a typical way this looks for a simple single for-loop data parallel kernel.

FOO::FOO(const RunParams& params)
  : KernelBase(rajaperf::Basic_Foo, params),
{
  setDefaultProblemSize(1000000);  // length of the for-loop
  setDefaultReps(1000);            // number of times the kernel will execute
                                   // to generate an execution run time value

  setActualProblemSize( getTargetProblemSize() );  // actual problem size may
                                                   // be different than the
                                                   // default size based on
                                                   // user-provided run time
                                                   // options

  setItsPerRep( getActualProblemSize() );
  setKernelsPerRep(1);
  setBytesPerRep( ... );  // value set based on data read and written when
                          // kernel executes
  setFLOPsPerRep( ... );  // value set based on floating-point operations
                          // performed when kernel executes

  setUsesFeature(Forall); // the kernel uses the RAJA::forall construct and
                          // no other RAJA features.

  setVariantDefined( Base_Seq );
  setVariantDefined( Lambda_Seq );
  setVariantDefined( RAJA_Seq );

  setVariantDefined( Base_OpenMP );
  // etc.
}

The class destructor doesn't have any requirements beyond freeing memory owned by the class object as needed. Often, it is empty.

setUp() method

The setUp() method is responsible for allocating and initializing data necessary to run the kernel for the variant specified by its variant ID argument. For example, a baseline variant may have aligned data allocation to help enable SIMD optimizations, an OpenMP variant may initialize arrays following a pattern of "first touch" based on how memory and threads are mapped to CPU cores, a CUDA variant may initialize data in host memory, which will be copied to device memory when a CUDA variant executes, etc.

It is important to use the same data allocation and initialization operations for all kernel variants so that checksums can be compared at the end of a run.

Note: to simplify these operations and help ensure consistency, there exist utility methods to allocate, initialize, deallocate, and copy data, and compute checksums defined in the DataUtils.hpp CudaDataUtils.hpp, OpenMPTargetDataUtils.hpp, etc. header files in the common directory.

run methods

Which files contain which 'run' methods and associated variant and tuning implementations is described above. Each method takes a variant ID argument which identifies the variant to be run and a tuning index which identifies the tuning of the variant to run. Note that the tuning index can be ignored when there is only one tuning. Each method is also responsible for calling base class methods to start and stop execution timers when a loop variant is run. A typical kernel execution code section may look like:

void Foo::runSeqVariant(VariantID vid, size_t /*tuning_idx*/)
{
  const Index_type run_reps = getRunReps();
  // ...

  switch ( vid ) {

    case Base_Seq : {

      startTimer();
      for (RepIndex_type irep = 0; irep < run_reps; ++irep) {

         // Implementation of Base_Seq kernel variant...

      }
      stopTimer();

      break;
    }

#if defined(RUN_RAJA_SEQ)
    case Lambda_Seq : {

      startTimer();
      for (RepIndex_type irep = 0; irep < run_reps; ++irep) {

        // Implementation of Lambda_Seq kernel variant...

      }
      stopTimer();

      break;
    }

    case RAJA_Seq : {

      startTimer();
      for (RepIndex_type irep = 0; irep < run_reps; ++irep) {

        // Implementation of RAJA_Seq kernel variant...

      }
      stopTimer();

      break;
    }
#endif // RUN_RAJA_SEQ

    default : {
      getCout() << "\n  <kernel-name> : Unknown variant id = " << vid << std::endl;
    }

  }
}

All kernel implementation files are organized in this way. So following this pattern will ensure all new additions are consistent.

Important notes:

As mentioned earlier, there are multiple source files for each kernel. The reason for this is that it makes it easier to apply unique compiler flags to different variants and to manage compilation and linking issues that arise when some kernel variants are combined in the same translation unit.
For convenience, we make heavy use of macros to define data declarations and kernel bodies in the Suite. While seemingly cryptic, this significantly reduces the amount of redundant code required to implement multiple variants for each kernel and make sure things are the same as much as possible. The kernel class implementation files in the Suite provide many examples of the basic pattern we use.
We also use macros to define some methods used with GPU block size tunings. While seemingly cryptic, this significantly reduces the amount of redundant code required to implement calling and naming each of the multiple tunings for each kernel and make sure things are the same as much as possible.

updateChecksum() method

The updateChecksum() method is responsible for adding the checksum for the current kernel (based on the data the kernel computes) to the checksum value for the variant and tuning of the kernel just executed, which is held in the KernelBase base class object.

It is important that the checksum be computed in the same way for each variant of the kernel so that checksums for different variants can be compared to help identify differences, and potential errors in implementations, compiler optimizations, programming model execution, etc.

Note: to simplify checksum computations and help ensure consistency, there are methods to compute checksums, a weighted sum of array values for example, are defined in the DataUtils.hpp header file in the common directory.

tearDown() method

The tearDown() method frees and/or resets all kernel data that is allocated and/or initialized in the setUp() method execution to prepare for other kernel variants run subsequently.

Add object construction operation

The Executor class in the common directory is responsible for creating kernel objects for the kernels to be run based on the Suite input options. To ensure a new kernel object will be created properly, add a call to its class constructor based on its KernelID in the getKernelObject() method in the RAJAPerfSuite.cpp file.

Adding a variant

Each variant in the RAJA Performance Suite is identified by an enumeration value and a string name. Adding a new variant requires adding these two items similarly to adding those for a kernel as described above.

Add the variant ID and name

First, add an enumeration value identifier for the variant, that is unique among all variants, in the enum 'VariantID' in the header file RAJAPerfSuite.hpp:

enum VariantID {
..
  NewVariant,
..
};

Second, add the variant name to the array of strings VariantNames in the file RAJAPerfSuite.cpp:

static const std::string VariantNames [] =
{
..
  std::string("NewVariant"),
..
};

Note that the variant string name is just a string version of the variant ID. This convention must be followed so that the variant works properly with the Performance Suite machinery. Also, the values in the VariantID enum and the strings in the VariantNames array must be kept consistent (i.e., same order and matching one-to-one).

Add kernel variant implementations

In the classes containing kernels to which the new variant applies, add implementations for the variant in the setup, kernel execution, checksum computation, and teardown methods as needed. Also, make sure to define the variant for those kernels in the kernel class constructors by calling setVariantDefined(NewVariant) so that the variant can be run. These operations are described in earlier sections for adding a new kernel above.

Add kernel tuning implementations

In the classes containing kernels to which the new tuning applies, add implementations for the tuning in the kernel execution and tuning naming methods as needed. Note that the tuning indices are determined by the order that the tuning names are added in the set*TuningDefinitions method. Therefore the run*Variant methods should have similar logic in order to run the correct tuning based on the index.

Continuous Integration

RAJAPerf Suite uses continuous integration to ensure that changes added to the repository are well integrated and tested for compatibility with the rest of the existing code base. Our CI tests include a variety of vetted configurations that run on different LC machines.

RAJAPerf Suite shares its Gitlab CI workflow with other projects. The documentation is therefore shared.

Contributions

The RAJA Performance Suite is a work-in-progress, with new kernels and variants added as new features and back-end support are developed in RAJA. We encourage interested parties to contribute to it so that C++ compiler optimizations and support for programming models like RAJA continue to improve.

The Suite developers follow the GitFlow development model. Folks wishing to contribute to the Suite, should include their work in a feature branch created from the Performance Suite develop branch. Then, create a pull request with the develop branch as the destination when it is ready to be reviewed. The develop branch contains the latest work in RAJA Performance Suite. Periodically, we merge the develop branch into the main branch and tag a new release.

If you would like to contribute to the RAJA Performance Suite, or have questions about doing so, please contact the maintainer of the Suite listed below.

Authors

The primary developer/maintainer of the RAJA Performance Suite:

Rich Hornung ([email protected])

Please see the {RAJA Performance Suite Contributors Page](https://github.com/LLNL/RAJAPerf/graphs/contributors), to see the full list of contributors to the project.

LICENSE

The RAJA Performance Suite is licensed under the BSD 3-Clause license, (BSD-3-Clause or https://opensource.org/licenses/BSD-3-Clause).

Copyrights and patents in the RAJAPerf project are retained by contributors. No copyright assignment is required to contribute to RAJAPerf.

Unlimited Open Source - BSD 3-clause Distribution LLNL-CODE-738930 OCEC-17-159

For release details and restrictions, please see the information in the following:

SPDX Usage

Individual files contain SPDX tags instead of the full license text. This enables machine processing of license information based on the SPDX License Identifiers that are available here: https://spdx.org/licenses/

Files that are licensed as BSD 3-Clause contain the following text in the license header:

SPDX-License-Identifier: (BSD-3-Clause)

External Packages

The RAJA Performance Suite has some external dependencies, which are included as Git submodules. These packages are covered by various permissive licenses. A summary listing follows. See the license included with each package for full details.

PackageName: BLT PackageHomePage: https://github.com/LLNL/blt/ PackageLicenseDeclared: BSD-3-Clause

PackageName: RAJA PackageHomePage: http://github.com/LLNL/RAJA/ PackageLicenseDeclared: BSD-3-Clause

Name		Name	Last commit message	Last commit date
Latest commit History 1,823 Commits
.github		.github
.gitlab		.gitlab
blt @ 8c22999		blt @ 8c22999
scripts		scripts
src		src
test		test
tpl		tpl
.gitignore		.gitignore
.gitlab-ci.yml		.gitlab-ci.yml
.gitmodules		.gitmodules
.travis.yml		.travis.yml
.uberenv_config.json		.uberenv_config.json
CMakeLists.txt		CMakeLists.txt
Dockerfile		Dockerfile
LICENSE		LICENSE
NOTICE		NOTICE
README.md		README.md
RELEASE		RELEASE
azure-pipelines.yml		azure-pipelines.yml

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asarkar-parsys/RAJAPerf

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