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Performance Modelling-Driven Optimization of RISC-V Hardware for Efficient SpMV

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High Performance Computing (ISC High Performance 2023)

Abstract

The growing need for inference on edge devices brings with it a necessity for efficient hardware, optimized for particular computational kernels, such as Sparse Matrix-Vector Multiplication (SpMV). With the RISC-V Instruction Set Architecture (ISA) providing unprecedented freedom to hardware designers, there is now a greater opportunity to tailor these microarchitectures to both the application requirements and the data it is expected to process. In this paper, we demonstrate the use of the insights provided by the Cache-Aware Roofline Model (CARM) in the hardware design process, optimizing a RISC-V architecture for efficient and performant execution of SpMV. Specifically, we assess the effect architectural parameters associated with the processor’s cache and floating-point unit have on the architecture and SpMV performance. Following a reparameterization closely guided by the CARM, we demonstrate a \(2.04\times \) improvement in performance and a significant decrease in underused computational resources.

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Acknowledgement

This project has received funding from the European High Performance Computing Joint Undertaking (JU) under Framework Partnership Agreement No 800928 and Specific Grant Agreement No 101036168 (EPI SGA2) and Grant agreement No 956213 (SparCity). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and from Croatia, France, Germany, Greece, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and Turkey. It also received funding from FCT (Fundação para a Ciência e a Tecnologia, Portugal), through the UIDB/50021/2020 project.

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Correspondence to Alexandre Rodrigues .

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Rodrigues, A., Sousa, L., Ilic, A. (2023). Performance Modelling-Driven Optimization of RISC-V Hardware for Efficient SpMV. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_36

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  • DOI: https://doi.org/10.1007/978-3-031-40843-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40842-7

  • Online ISBN: 978-3-031-40843-4

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