RAJA is a library of C++ software abstractions, primarily developed at Lawrence Livermore National Laboratory (LLNL), that enables architecture and programming model portability for HPC applications. RAJA has two main goals:
- To enable application portability with manageable disruption to existing algorithms and programming styles
- To achieve performance comparable to using common programming models, such as OpenMP, CUDA, etc. directly.
RAJA offers portable, parallel loop execution by providing building blocks that extend the generally-accepted parallel for idiom. RAJA relies on standard C++14 features.
RAJA's design is rooted in decades of experience working on production mesh-based multiphysics applications. Based on the diversity of algorithms and software engineering styles used in such applications, RAJA is designed to enable application developers to adapt RAJA concepts and specialize them for different code implementation patterns and C++ usage.
RAJA shares goals and concepts found in other C++ portability abstraction approaches, such as Kokkos and Thrust. However, it includes concepts and capabilities that are absent in other models that are fundamental to applications we work with.
It is important to note that, although RAJA is used in a diversity of production applications, it is very much a work-in-progress. The community of researchers and application developers at LLNL that actively contribute to it is growing. Versions available as GitHub releases contain mostly well-used and well-tested features. Our core interfaces are fairly stable while underlying implementations are being refined. Additional features will appear in future releases.
The RAJA code lives in a GitHub repository. To clone the repo, use the command:
git clone --recursive https://github.com/llnl/raja.git
Then, you can build RAJA like any other CMake project, provided you have a C++ compiler that supports the C++14 standard. The simplest way to build the code, using your system default compiler, is to run the following sequence of commands in the top-level RAJA directory (in-source builds are not allowed!):
mkdir build
cd build
cmake ../
make
More details about RAJA configuration options are located in the RAJA User Guide (linked below).
We also maintain a RAJA Template Project that shows how to use RAJA in a CMake project, either as a Git submodule or as an installed library.
The RAJA User Guide is the best place to start learning about RAJA and how to use it.
The most recent version of the User Guide (RAJA develop branch): https://raja.readthedocs.io
To access docs for other RAJA released versions: https://readthedocs.org/projects/raja/
To cite RAJA, please use the following references:
-
RAJA Performance Portability Layer. https://github.com/LLNL/RAJA
-
D. A. Beckingsale, J. Burmark, R. Hornung, H. Jones, W. Killian, A. J. Kunen, O. Pearce, P. Robinson, B. S. Ryujin, T. R. W. Scogland, "RAJA: Portable Performance for Large-Scale Scientific Applications", 2019 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC). Download here
The RAJA Performance Suite contains a collection of loop kernels implemented in multiple RAJA and non-RAJA variants. We use it to monitor and assess RAJA performance on different platforms using a variety of compilers. Many major compiler vendors use the Suite to improve their support of abstractions like RAJA.
The RAJA Proxies repository contains RAJA versions of several important HPC proxy applications.
CHAI provides a managed array abstraction that works with RAJA to automatically copy data used in RAJA kernels to the appropriate space for execution. It was developed as a complement to RAJA.
The most effective way to communicate with the core RAJA development team is via our mailing list: [email protected]
You are also welcome to join our RAJA Google Group.
If you have questions, find a bug, or have ideas about expanding the functionality or applicability of RAJA and are interested in contributing to its development, please do not hesitate to contact us. We are very interested in improving RAJA and exploring new ways to use it.
The RAJA team follows the GitFlow development model. Folks wishing to contribute to RAJA, should
include their work in a feature branch created from the RAJA develop branch.
That branch contains the latest work in RAJA. Then, create a pull request
with the develop branch as the destination. Periodically, we merge the
develop branch into the main branch and tag a new release.
Please see the RAJA Contributors Page, to see the full list of contributors to the project.
RAJA is licensed under the BSD 3-Clause license.
Copyrights and patents in the RAJA project are retained by contributors. No copyright assignment is required to contribute to RAJA.
Unlimited Open Source - BSD 3-clause Distribution
LLNL-CODE-689114 OCEC-16-063
For release details and restrictions, please see the information in the following:
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)
RAJA bundles its external dependencies as submodules in the git repository. 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: camp
PackageHomePage: https://github.com/LLNL/camp
PackageLicenseDeclared: BSD-3-Clause
PackageName: CUB
PackageHomePage: https://github.com/NVlabs/cub
PackageLicenseDeclared: BSD-3-Clause
PackageName: rocPRIM
PackageHomePage: https://github.com/ROCmSoftwarePlatform/rocPRIM.git
PackageLicenseDeclared: MIT License