@rhornung67 rhornung67 released this Feb 7, 2019

This release contains several major changes, new features, a variety of bugfixes, and expanded user documentation and accompanying example codes. For more information and details about any of the changes listed below, please consult the RAJA documentation for the 0.7.0 release which is linked to our Github project.

Please download the RAJA-0.7.0.tar.gz file above. The others will not work due to the way RAJA uses git submodules.

Major changes include:

  • RAJA::forallN methods were marked deprecated in the 0.6.0 release. They have been removed. All applications that contain nested loops and have been using forallN methods should convert them to use the RAJA::kernel interface.
  • RAJA::forall methods that take explicit loop bounds rather than segments (e.g., RAJA::forall(beg, end, ...) were marked deprecated in the 0.6.0 release. They have been removed. Hopefully, this will result in faster compile times due to simpler template resolution. Users who have been passing loop bounds directly to forall methods should convert those cases to use RAJA segments instead.
  • CUDA execution policies for use in RAJA::kernel policies have been significantly reworked and redefined. The new set of policies are much more flexible and provide improved run time performance.
  • New, improved support for loop tiling algorithms and support for CPU cache blocking, CUDA GPU thread local data and shared memory is available. This includes RAJA::kernel policy statement types to make tile numbers and local tile indices available in user kernels (TileTCount and ForICount statement types), and a new RAJA::LocalArray type with various CPU and GPU memory policies. Due to these new features, RAJA 'shmemwindow' statements have been removed.
  • This release contains expanded documentation and example codes for the RAJA::kernel interface, including loop tiling algorithms and support for CPU cache blocking, CUDA GPU thread local data and shared memory.

Other notable changes include:

  • Features:

    • Initial support for OpenMP target execution policies with RAJA::kernel added.
    • The RAJA::AtomicRef interface is now consistent with the C++20 std::atomic_ref interface.
    • Atomic compare-exchange operations added.
    • CUDA reduce policies no longer require a thread-block size parameter.
    • New features considered prelimiary with no significant documentation or examples available yet:
      • RAJA::statement::Reduce type for use in RAJA::kernel execution policies. This enables the ability to perform reductions and access reduced values inside user kernels.
      • Warp-level execution policies added for CUDA.
  • Performance improvements:

    • Better use of inline directives to improve likelihood of SIMD instruction generation with the Intel compiler.
  • Bug fixes:

    • Several CHAI integration issues resolved.
    • Resolve issue with alignx directive when using XL compiler as host compiler with CUDA.
    • Fix issue associated with how XL compiler interprets OpenMP region definition.
    • Various tweaks to camp implementation to improve robustness.
  • Build changes/improvements:

    • The minimum required version of CMake has changed to 3.8 for all programming model back-ends, except CUDA. The minimum CMake version for CUDA support is 3.9.
    • Improved support for clang-cuda compiler. Some features still do not work with that compiler.
    • Update NVIDIA cub module to version 1.8.0.
    • Enable use of 'BLT_SOURCE_DIR' CMake variable to help prevent conflicts with BLT versions in RAJA and other libraries used in applications.
Assets 3