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GPU linear and non-linear Poisson-Boltzmann solver module for DelPhi.

José Colmenares1, Jesús Ortiz, Walter Rocchia

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Summary
This summary is machine-generated.

We developed a faster GPU-accelerated Poisson-Boltzmann solver for biomolecular electrostatics. This CUDA implementation achieves a tenfold speedup, enhancing computational biology research.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Scientific Computing

Background:

  • The Poisson-Boltzmann equation is crucial for modeling electrostatic interactions in biological systems.
  • Efficient solvers are needed to handle the computational demands of biomolecular electrostatics.
  • Existing methods may not fully leverage modern hardware for performance gains.

Purpose of the Study:

  • To implement a high-performance Poisson-Boltzmann equation solver utilizing CUDA for GPU acceleration.
  • To integrate the solver with the widely-used DelPhi software for biomolecular electrostatics.
  • To evaluate the performance and efficiency of the GPU-based implementation.

Main Methods:

  • A finite difference scheme was adapted for GPU architecture using CUDA.
  • Double-precision calculations were employed for accuracy in both linear and non-linear Poisson-Boltzmann equations.
  • The solver was interfaced with the DelPhi electrostatics software package.

Main Results:

  • A CUDA-based GPU implementation of the Poisson-Boltzmann solver was successfully developed.
  • The solver demonstrated a significant speedup of approximately 10 times compared to CPU-based methods.
  • Performance was validated on representative biological systems.

Conclusions:

  • The CUDA-based GPU implementation offers a substantial performance improvement for Poisson-Boltzmann equation solving.
  • This accelerated solver enhances the capabilities of computational biology tools like DelPhi.
  • The open-source availability facilitates broader adoption and further research in biomolecular electrostatics.