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

Phillip Duncan-Gelder1,2, Darin O'Keeffe3,4, Philip J Bones3,5

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Barracuda, a new virtual machine, enhances GPU simulations for dynamic biological processes. This innovation improves accuracy in biomedical research by enabling real-time parameter changes in complex simulations.

Keywords:
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Area of Science:

  • Biomedical simulation
  • Computational biology
  • GPU computing

Background:

  • Accurate simulation of dynamic biological phenomena is vital for biomedical research and diagnostics.
  • Traditional static GPU environments (CUDA) lack flexibility for evolving parameters, hindering clinical applications.
  • Need for adaptable GPU frameworks to model dynamic biological processes.

Purpose of the Study:

  • Introduce Barracuda, an open-source, lightweight, header-only virtual machine for GPU integration.
  • Enable real-time parameter perturbations in GPU simulations for enhanced flexibility.
  • Facilitate integration into biomedical workflows via a high-level language and Rust compiler.

Main Methods:

  • Developed Barracuda as a Turing-complete virtual machine with a C/CUDA library.
  • Created a high-level programming language and Rust-based compiler for accessibility.
  • Validated computational completeness with Rule 110 cellular automaton and Mandelbrot set computations.
  • Demonstrated dynamic parameter recalculation in magnetic resonance imaging (MRI) simulations.

Main Results:

  • Barracuda confirmed Turing completeness and versatility through benchmark validations.
  • Enabled dynamic recalculation of key MRI parameters (e.g., T1 relaxation, off-resonance frequencies).
  • Showcased improved simulation accuracy for dynamic biological processes despite computational overhead.
  • Modular architecture supports incremental integration and rapid prototyping.

Conclusions:

  • Barracuda offers a flexible solution for dynamic simulations in biomedical research.
  • Enhances GPU simulation capabilities by bridging static programming and dynamic modeling.
  • Future work will focus on performance optimization and expanding instruction sets.