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Merge branch 'develop' into artv3/caliper-support
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.gitlab/custom-jobs-and-variables.yml

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# Lassen and Butte use a different job scheduler (spectrum lsf) that does not
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# allow pre-allocation the same way slurm does.
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# Arguments for job level allocation
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LASSEN_JOB_ALLOC: "1 -W 40 -q pci"
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LASSEN_JOB_ALLOC: "1 -W 45 -q pci"
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# Project specific variants for lassen
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PROJECT_LASSEN_VARIANTS: "~shared +openmp +vectorization +tests cuda_arch=70"
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# Project specific deps for lassen

CMakeLists.txt

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include(CMakeDependentOption)
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# Set version number
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set(RAJA_VERSION_MAJOR 2024)
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set(RAJA_VERSION_MINOR 07)
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set(RAJA_VERSION_MAJOR 2025)
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set(RAJA_VERSION_MINOR 03)
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set(RAJA_VERSION_PATCHLEVEL 0)
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if (RAJA_LOADED AND (NOT RAJA_LOADED STREQUAL "${RAJA_VERSION_MAJOR}.${RAJA_VERSION_MINOR}.${RAJA_VERSION_PATCHLEVEL}"))

RELEASE

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*******************************************************************************
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RAJA: ................................, version 2023.06.1
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RAJA: ................................, version 2025.03.0
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Copyright (c) 2016-25, Lawrence Livermore National Security, LLC.
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Produced at the Lawrence Livermore National Laboratory.

RELEASE_NOTES.md

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* Bug fixes/improvements:
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Version 2025.03.0 -- Release date 2025-03-17
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============================================
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This release contains new features, bug fixes, and updates to submodule
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dependencies.
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Notable changes include:
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* New features / API changes:
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* Added improved support for perfectly nested loops in RAJA::launch.
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* Added helper methods to simplify the creation of RAJA View objects
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with permutations of stride ordering. Examples and user docs have also
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been added.
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* Added GPU policies for CUDA and HIP that do not check loop bounds when
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they do not need to be checked in a kernel. This can help improve
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performance by up to 5%. The new policies are documented in the RAJA
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user guide and include `direct_unchecked` in their names.
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* Refactored the new (experimental) RAJA reduction interface to have
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consistent min/max/loc operator semantics and added type safety to
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reduce erroneous usage. Changes are described in the RAJA User Guide.
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* Added support for new RAJA reduction interface to RAJA::dynamic_forall
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and pulled dynamic_forall out of RAJA `expt` namespace.
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* Added `RAJA_HIP_WAVESIZE` CMake option to set the wave size for HIP
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builds. It defaults to 64 but can be set to 32, for example, to
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build RAJA to run on Radeon gaming cards.
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* Build changes/improvements:
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* Update BLT to v0.7.0 release.
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* Update camp submodule to v2025.03.0 release.
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* Update desul submodule to 6114dd25b54782678c555c0c1d2197f13cc8d2a0
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commit.
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* Added clang-format CI check (clang 14) that must pass before a PR can
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be merged -- noted here so external contributors are aware.
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* Bug fixes/improvements:
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* Resolved undefined behavior related to constructing
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uniform_int_distribution with min > max. This was causing some Windows
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tests to fail.
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* Corrected call to wrong global function when using a fixed CUDA policy
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and reductions in RAJA::launch kernel -- potential performance issue.
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* Fixed memory leak in RAJA::launch OpenMP back-end.
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* Added missing host-device decorations to some math utility functions.
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* Fixed MSVC compilation failures with 64-bit intrinsics in x86 Windows
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builds.
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* Fixed issue so that a kernel will no longer be launched when there is no
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work for it to do; i.e., no active iteration space entries.
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* Removed invalid C++ usage in implementation of RAJA::kernel `initLocalMem`
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statement, which was causing large warning messages during compilation.
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Version 2024.07.0 -- Release date 2024-07-24
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============================================
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blt

Submodule blt updated 354 files

docs/conf.py

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# built documents.
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#
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# The short X.Y version.
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version = u'2024.07'
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version = u'2025.03'
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# The full version, including alpha/beta/rc tags.
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release = u'2024.07.0'
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release = u'2025.03.0'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.

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