- update to 5.7.0:
* A new NVECTOR implementation based on the SYCL abstraction layer has been added
targeting Intel GPUs. At present the only SYCL compiler supported is the DPC++
(Intel oneAPI) compiler. See the SYCL NVECTOR section in the user guide for
more details. This module is considered experimental and is subject to major
changes even in minor releases.
* A new SUNMatrix and SUNLinearSolver implementation were added to interface with
the MAGMA linear algebra library. Both the matrix and the linear solver support
general dense linear systems as well as block diagonal linear systems, and both
are targeted at GPUs (AMD or NVIDIA).
* Fixed a bug in the SUNDIALS CMake which caused an error if the
CMAKE_CXX_STANDARD and SUNDIALS_RAJA_BACKENDS options were not provided.
* Fixed some compiler warnings when using the IBM XL compilers.
* A new NVECTOR implementation based on the AMD ROCm HIP platform has been added.
This vector can target NVIDIA or AMD GPUs. See HIP NVECTOR section in the user
guide for more details. This module is considered experimental and is subject
to change from version to version.
* The RAJA NVECTOR implementation has been updated to support the HIP backend in
addition to the CUDA backend. Users can choose the backend when configuring
SUNDIALS by using the `SUNDIALS_RAJA_BACKENDS` CMake variable. This module
remains experimental and is subject to change from version to version.
* A new optional operation, `N_VGetDeviceArrayPointer`, was added to the N_Vector
API. This operation is useful for N_Vectors that utilize dual memory spaces,
e.g. the native SUNDIALS CUDA N_Vector.
* The SUNMATRIX_CUSPARSE and SUNLINEARSOLVER_CUSOLVERSP_BATCHQR implementations
no longer require the SUNDIALS CUDA N_Vector. Instead, they require that the
vector utilized provides the `N_VGetDeviceArrayPointer` operation, and that the
pointer returned by `N_VGetDeviceArrayPointer` is a valid CUDA device pointer.