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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2309.16509 (cs)
[Submitted on 28 Sep 2023]

Title:SIMD Everywhere Optimization from ARM NEON to RISC-V Vector
Extensions

Authors:Ju-Hung Li, Jhih-Kuan Lin, Yung-Cheng Su, Chi-Wei Chu,
Lai-Tak Kuok, Hung-Ming Lai, Chao-Lin Lee, Jenq-Kuen Lee
Download a PDF of the paper titled SIMD Everywhere Optimization from
ARM NEON to RISC-V Vector Extensions, by Ju-Hung Li and 7 other
authors
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    Abstract:Many libraries, such as OpenCV, FFmpeg, XNNPACK, and
    Eigen, utilize Arm or x86 SIMD Intrinsics to optimize programs
    for performance. With the emergence of RISC-V Vector Extensions
    (RVV), there is a need to migrate these performance legacy codes
    for RVV. Currently, the migration of NEON code to RVV code
    requires manual rewriting, which is a time-consuming and
    error-prone process. In this work, we use the open source tool,
    "SIMD Everywhere" (SIMDe), to automate the migration. Our primary
    task is to enhance SIMDe to enable the conversion of ARM NEON
    Intrinsics types and functions to their corresponding RVV
    Intrinsics types and functions. For type conversion, we devise
    strategies to convert Neon Intrinsics types to RVV Intrinsics by
    considering the vector length agnostic (vla) architectures. With
    function conversions, we analyze commonly used conversion methods
    in SIMDe and develop customized conversions for each function
    based on the results of RVV code generations. In our experiments
    with Google XNNPACK library, our enhanced SIMDe achieves speedup
    ranging from 1.51x to 5.13x compared to the original SIMDe, which
    does not utilize customized RVV implementations for the
    conversions.

Subjects: Distributed, Parallel, and Cluster Computing (cs.DC);
          Programming Languages (cs.PL)
Cite as:  arXiv:2309.16509 [cs.DC]
          (or arXiv:2309.16509v1 [cs.DC] for this version)
          https://doi.org/10.48550/arXiv.2309.16509
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chao-Lin Lee [view email]
[v1] Thu, 28 Sep 2023 15:14:22 UTC (1,094 KB)
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