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Abstract <p>Accurate simulation of dynamic biological phenomena, such as tissue response and disease progression, is crucial in biomedical research and diagnostics. Traditional GPU-based simulation frameworks, typically static CUDA<sup>®</sup> environments, struggle with dynamically evolving parameters, limiting flexibility and clinical applicability. We introduce Barracuda, an open-source, lightweight, header-only, Turing-complete virtual machine designed for seamless integration into GPU environments. Barracuda enables real-time parameter perturbations through an expressive instruction set and operations library, implemented in a compact C/CUDA library. A dedicated high-level programming language and Rust-based compiler enhance accessibility, allowing straightforward integration into biomedical simulation workflows. Benchmark validations, including Rule 110 cellular automaton and Mandelbrot computations, confirm Barracuda’s versatility and computational completeness. In magnetic resonance imaging (MRI) simulations, Barracuda allows for the dynamic recalculation of critical parameters, such as <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(T_1\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> relaxation times and temperature-induced off-resonance frequencies. Although it introduces computational overhead compared to static kernels, Barracuda significantly improves simulation accuracy by enabling dynamic modeling of key biological processes. Barracuda’s modular architecture supports incremental integration, providing valuable flexibility for biomedical research and rapid prototyping. Future developments aim to optimize performance and expand domain-specific instruction sets, reinforcing Barracuda’s role in bridging static GPU programming and dynamic simulation requirements.</p> Graphic abstract <p></p>

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Barracuda: a dynamic, Turing-complete GPU virtual machine for high-performance simulations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月13日

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  • Phillip Duncan-Gelder,
  • Darin O’Keeffe,
  • Philip J. Bones,
  • Steven Marsh

摘要

Abstract

Accurate simulation of dynamic biological phenomena, such as tissue response and disease progression, is crucial in biomedical research and diagnostics. Traditional GPU-based simulation frameworks, typically static CUDA® environments, struggle with dynamically evolving parameters, limiting flexibility and clinical applicability. We introduce Barracuda, an open-source, lightweight, header-only, Turing-complete virtual machine designed for seamless integration into GPU environments. Barracuda enables real-time parameter perturbations through an expressive instruction set and operations library, implemented in a compact C/CUDA library. A dedicated high-level programming language and Rust-based compiler enhance accessibility, allowing straightforward integration into biomedical simulation workflows. Benchmark validations, including Rule 110 cellular automaton and Mandelbrot computations, confirm Barracuda’s versatility and computational completeness. In magnetic resonance imaging (MRI) simulations, Barracuda allows for the dynamic recalculation of critical parameters, such as \(T_1\) T 1 relaxation times and temperature-induced off-resonance frequencies. Although it introduces computational overhead compared to static kernels, Barracuda significantly improves simulation accuracy by enabling dynamic modeling of key biological processes. Barracuda’s modular architecture supports incremental integration, providing valuable flexibility for biomedical research and rapid prototyping. Future developments aim to optimize performance and expand domain-specific instruction sets, reinforcing Barracuda’s role in bridging static GPU programming and dynamic simulation requirements.

Graphic abstract