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https://github.com/LLNL/RAJA

RAJA Performance Portability Layer (C++)
https://github.com/LLNL/RAJA

blt c-plus-plus cpp llnl parallel-computing portability programming-model radiuss raja

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RAJA Performance Portability Layer (C++)

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README 

        

[comment]: # (#################################################################)

[comment]: # (Copyright 2016-24, Lawrence Livermore National Security, LLC)

[comment]: # (and RAJA project contributors. See the RAJA/LICENSE file)

[comment]: # (for details.)

[comment]: # 

[comment]: # (# SPDX-License-Identifier: BSD-3-Clause)

[comment]: # (#################################################################)



# RAJA



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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](https://github.com/kokkos/kokkos)

and [Thrust](https://developer.nvidia.com/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](https://github.com/LLNL/RAJA/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.



Quick Start

-----------



The RAJA code lives in a GitHub [repository](https://github.com/llnl/raja).

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**](https://github.com/LLNL/RAJA-project-template) that shows how to use RAJA in a CMake project, either as a Git

submodule or as an installed library.



User Documentation and Tutorials

--------------------------------



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/



We maintain a repository for the RAJA portaility suite tutorial material: https://github.com/LLNL/raja-suite-tutorial



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](https://conferences.computer.org/sc19w/2019/#!/toc/14)



Related Software

--------------------



The [**RAJA Performance Suite**](https://github.com/LLNL/RAJAPerf) 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

Performance Suite is an excellent source of examples of RAJA usage where you

can compare RAJA and non-RAJA variants of a variety of different kernels and

RAJA back-ends.**



The [**RAJA Proxies**](https://github.com/LLNL/RAJAProxies) repository 

contains RAJA versions of several important HPC proxy applications.



[**CHAI**](https://github.com/LLNL/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.



Communicate with Us

-------------------



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](https://groups.google.com/forum/#!forum/raja-users).



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.



Contributions

---------------



The RAJA team follows the [GitFlow](http://nvie.com/posts/a-successful-git-branching-model/) 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.



Authors

-----------



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



License

-----------



RAJA is licensed under the [BSD 3-Clause license](https://opensource.org/licenses/BSD-3-Clause).



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:

- [RELEASE](./RELEASE)

- [LICENSE](./LICENSE)

- [NOTICE](./NOTICE)



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

-------------------

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