RISC-V Acceleration for Linear Algebra Problems: Eigen Library Enhancements with RVV Support

2026
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Authors: Valeria Puzikova; Andrey Sokolov; Ksenia Zaytseva
Published in: Parallel Computing Technologies
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the optimization of the Eigen linear algebra library for the RISC-V architecture, focusing on the RISC-V Vector Extension (RVV). It begins by discussing the importance of high-performance mathematical libraries in machine learning frameworks and the need for optimizations tailored to specific architectures. The text then explores the architecture of the Eigen library, highlighting its features and the challenges of adding low-level optimizations for RISC-V. The core of the chapter is dedicated to the implementation of RVV support in Eigen, including the use of intrinsic wrappers and the optimization of various data types. The experimental results section compares the performance of Eigen with RVV optimizations against OpenBLAS and auto-vectorization, demonstrating significant speedups. The chapter concludes with a discussion on the benefits of the chosen optimization approach and future work to further enhance Eigen's performance on RISC-V.

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Metadata
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Title: RISC-V Acceleration for Linear Algebra Problems: Eigen Library Enhancements with RVV Support
Authors: Valeria Puzikova
Andrey Sokolov
Ksenia Zaytseva
Publisher: Springer Nature Switzerland
Book: Parallel Computing Technologies
Print ISBN: 978-3-032-06750-0

Electronic ISBN: 978-3-032-06751-7

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-06751-7_13

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