GPU vendors provide highly optimized libraries for math operations such as fast Fourier transformation or linear algebra (FFT, (sparse)BLAS/LAPACK, …) to perform those on devices. And OpenMP is a popular, vendor-agnostic method for parallelization on the CPU but increasingly also for offloading calculations to the GPU.

This talk shows how OpenMP can be used to reduce to reduce vendor-specific code, make calling it more convenient, and to combine OpenMP offloading with those libraries. While the presentation illustrates the use with the GNU Compiler Collection (GCC), the feature is a generic feature of OpenMP 5.2, extended in 6.0, and is supported by multiple compilers.

In terms of OpenMP features, the 'interop' directive provides the interoperability support, the 'declare variant' directive with the 'adjust_args' and 'append_args' clauses enable to write neater code; means for memory allocation and memory transfer and running code blocks on the GPU ('target' construct) complete the required feature set.

• The OpenMP specification, current, past and future version, errata and example documents can be found at https://www.openmp.org/specifications/; a list of compilers and tools for OpenMP is at https://www.openmp.org/resources/openmp-compilers-tools/
• GCC's OpenMP documentation is available at https://gcc.gnu.org/onlinedocs/libgomp/ (API routines, implementation status, …) and, in particular, the supported interop foreign runtimes are documented at https://gcc.gnu.org/onlinedocs/libgomp/Offload-Target-Specifics.html; GCC supports offloading to Nvidia and AMD GPUs. GCC supports OpenMP interop since GCC 15, including most of the OpenMP 6.0 additions, including the Fortran API routines.