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Case Study for Running Memory-Bound Kernels on RISC-V CPUs

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 14098)


The emergence of a new, open, and free instruction set architecture, RISC-V, has heralded a new era in microprocessor architectures. Starting with low-power, low-performance prototypes, the RISC-V community has a good chance of moving towards fully functional high-end microprocessors suitable for high-performance computing. Achieving progress in this direction requires comprehensive development of the software environment, namely operating systems, compilers, mathematical libraries, and approaches to performance analysis and optimization. In this paper, we analyze the performance of two available RISC-V devices when executing three memory-bound applications: a widely used STREAM benchmark, an in-place dense matrix transposition algorithm, and a Gaussian Blur algorithm. We show that, compared to x86 and ARM CPUs, RISC-V devices are still expected to be inferior in terms of computation time but are very good in resource utilization. We also demonstrate that well-developed memory optimization techniques for x86 CPUs improve the performance on RISC-V CPUs. Overall, the paper shows the potential of RISC-V as an alternative architecture for high-performance computing.


  • High-Performance Computing
  • RISC-V
  • ISA
  • C++
  • Performance Analysis and Optimization
  • Memory-Bound Applications

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    In the case of using processors with RISC-V architecture, the OpenCV computation time was measured on a Linux image that supports vector instructions.


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The study is supported by the Lobachevsky University academic leadership program “Priority-2030”. Experiments were performed on the Lobachevsky supercomputer.

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Correspondence to Iosif  Meyerov .

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 Volokitin, V.,  Kozinov, E.,  Kustikova, V.,  Liniov, A.,  Meyerov, I. (2023). Case Study for Running Memory-Bound Kernels on RISC-V CPUs. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2023. Lecture Notes in Computer Science, vol 14098. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41672-9

  • Online ISBN: 978-3-031-41673-6

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