Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos
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| Format: | Preprint |
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2024
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| _version_ | 1866915102619336704 |
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| author | Diehl, Patrick Syskakis, Panagiotis Daiß, Gregor Brandt, Steven R. Kheirkhahan, Alireza Singanaboina, Srinivas Yadav Marcello, Dominic Taylor, Chris Leidel, John Kaiser, Hartmut |
| author_facet | Diehl, Patrick Syskakis, Panagiotis Daiß, Gregor Brandt, Steven R. Kheirkhahan, Alireza Singanaboina, Srinivas Yadav Marcello, Dominic Taylor, Chris Leidel, John Kaiser, Hartmut |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_00026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos Diehl, Patrick Syskakis, Panagiotis Daiß, Gregor Brandt, Steven R. Kheirkhahan, Alireza Singanaboina, Srinivas Yadav Marcello, Dominic Taylor, Chris Leidel, John Kaiser, Hartmut Distributed, Parallel, and Cluster Computing 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. |
| title | Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.00026 |