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GPU technology as a platform for accelerating physiological systems modeling based on Laguerre-Volterra networks.

Agathoklis Papadopoulos, Kyriaki Kostoglou, Georgios D Mitsis

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
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    Summary

    We developed a faster, GPU-accelerated model for biological system analysis using Laguerre-Volterra networks. This approach speeds up complex nonlinear modeling, improving computational efficiency for biomedical engineering applications.

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    Area of Science:

    • Biomedical Engineering
    • Computational Biology
    • Scientific Computing

    Background:

    • Mathematical modeling of biological systems is crucial but computationally intensive, often requiring nonlinear approaches with many parameters.
    • General-Purpose Graphics Unit Processing (GPGPU) programming, using CUDA-enabled algorithms on GPU cards, offers significant computational speedups.
    • Existing models may not fully leverage the parallel processing capabilities of GPUs for complex biological simulations.

    Purpose of the Study:

    • To develop a CUDA-enabled version of a nonlinear identification model for biological applications.
    • To implement Laguerre-Volterra networks, combining basis expansions and polynomial-type networks, on a GPGPU platform.
    • To evaluate the performance improvements of the GPU-based model compared to traditional implementations.

    Main Methods:

    • Developed a software implementation utilizing the GPGPU programming paradigm with CUDA.
    • Integrated a nonlinear identification approach combining basis expansions and polynomial-type networks (Laguerre-Volterra networks).
    • Executed model calculations on the GPU card of a host computer system to exploit inherent parallelism.

    Main Results:

    • The CUDA-enabled Laguerre-Volterra network model demonstrated performance improvements over the original MATLAB implementation.
    • GPU acceleration significantly reduced computation time for nonlinear modeling in biological applications.
    • The parallel nature of the modeling approach is well-suited for GPGPU acceleration.

    Conclusions:

    • The GPGPU implementation of Laguerre-Volterra networks provides a computationally efficient solution for complex biological modeling.
    • This approach enhances the feasibility of detailed mathematical modeling in biomedical engineering and biology.
    • Further applications of GPU acceleration in computational biology are warranted.