Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units
Ruxi Qi, Wesley M. Botello-Smith, and Ray Luo

TL;DR
This paper demonstrates how GPU acceleration, especially with Jacobi-preconditioned conjugate gradient solvers, significantly speeds up linear finite-difference Poisson-Boltzmann computations in biomolecular modeling.
Contribution
It introduces GPU-optimized implementations of linear PBE solvers, analyzing their performance and efficiency with various preconditioners and matrix formats.
Findings
Jacobi-preconditioned CG on GPU outperforms CPU solvers
Diagonal matrix format yields best efficiency for finite-difference systems
Single precision GPU computations maintain good numerical accuracy
Abstract
Electrostatic interactions play crucial roles in biophysical processes such as protein folding and molecular recognition. Poisson-Boltzmann equation (PBE)-based models have emerged as widely used in modeling these important processes. Though great efforts have been put into developing efficient PBE numerical models, challenges still remain due to the high dimensionality of typical biomolecular systems. In this study, we implemented and analyzed commonly used linear PBE solvers for the ever-improving graphics processing units (GPU) for biomolecular simulations, including both standard and preconditioned conjugate gradient (CG) solvers with several alternative preconditioners. Our implementation utilizes standard Nvidia CUDA libraries cuSPARSE, cuBLAS, and CUSP. Extensive tests show that good numerical accuracy can be achieved given that the single precision is often used for numerical…
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Taxonomy
TopicsAdvanced NMR Techniques and Applications · Protein Structure and Dynamics · Tensor decomposition and applications
