Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos
<|reference_start|>Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos: In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance Computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory references.   In this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std::experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX system.<|reference_end|>
@article{diehl2024preparing,
    title={Preparing for HPC on RISC-V: Examining Vectorization and Distributed
  Performance of an Astrophyiscs Application with HPX and Kokkos},
    author={Patrick Diehl and Panagiotis Syskakis and Gregor Dai{ss} and Steven
  R. Brandt and Alireza Kheirkhahan and Srinivas Yadav Singanaboina and Dominic
  Marcello and Chris Taylor and John Leidel and Hartmut Kaiser},
    journal={arXiv preprint arXiv:2407.00026},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00026},
    primaryClass={cs.DC}
}