![]() The new PBE solver will significantly improve the computational throughput for a range of biomolecular applications on the GPU platforms. We further show how to balance robustness and efficiency by utilizing MG’s overall efficiency and conjugate gradient’s robustness, pointing to a hybrid GPU solver with a good balance of efficiency and accuracy. Our analysis shows that robustness is a more pronounced issue than efficiency for both MG and other tested solvers when the single precision is used for complex biomolecules. Here we present an implementation and a thorough analysis of MG on GPUs. The robustness and efficiency of MG on GPUs are also unclear. This is not a surprise as it is a more complex method and depends on simpler but limited iterative methods such as Gauss-Seidel in its core relaxation procedure. On the other hand, geometric multigrid (MG) has been shown to be an optimal solver on CPUs, though no MG was reported for biomolecular applications on GPUs. However, neither convergence nor scaling properties of the two methods are optimal for large biomolecules. Efforts have been reported to develop PBE solvers on graphics processing units (GPUs) for efficient modeling of biomolecules, though only relatively simple successive over-relaxation and conjugate gradient methods were implemented. Fast convergence of PBE solvers is crucial in binding affinity computations as numerous snapshots need to be processed. Poisson-Boltzmann equation (PBE)-based continuum electrostatics models have been widely used in modeling electrostatic interactions in biochemical processes, particularly in estimating protein-ligand binding affinities. ![]()
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