使用Bempp-Exafmm进行病毒规模静电模拟的高生产率、高性能工作流程
High-productivity, high-performance workflow for virus-scale electrostatic simulations with Bempp-Exafmm
摘要：
生物分子静电是蛋白质功能和影响蛋白质功能的化学过程的关键。通过泊松-玻尔兹曼（PB）方程的隐式溶剂模型提供了比原子模型更少的计算成本，使更多研究人员能够进行病毒规模的大系统研究。在这里，我们提出了一个高生产力和高性能的线性PB求解器的基础上Exafmm，一个快速多极子方法库，和Bempp，Galerkin边界元方法包。该工作流程集成了一个易于使用的Python界面和优化的计算内核，并可以通过Quixyter笔记本交互式运行，以实现更快的原型设计。我们的结果显示了软件的能力，确认了代码的正确性，并评估了8,000到200万个元素的性能。展示了这个交互式计算平台的强大功能，我们只需几行代码就可以研究边界积分公式的两个变体的条件。网格细化研究确认收敛为$1/N$，对于$N$边界元素，并与使用各种蛋白质的可信APBS代码的结果进行比较，结果显示一致。我们使用9种不同的复合物的结合能计算与使用其他五个基于网格的PB求解器的结果一致。性能结果包括时间、故障和计算复杂度。Exafmm提供了几秒钟的评估速度，数千万个点，和$\mathcal{O}(N)$缩放。在我们与APBS的性能比较中观察到的趋势表明，Bempp-Exafmm在涉及较大结构或需要更高精度的应用中具有优势。计算寨卡病毒的溶剂化自由能（由160万个原子和1000万个边界元素表示）在单个计算节点（双20核）上花费了80分钟的运行时间。
英文摘要：
Biomolecular electrostatics is key in protein function and the chemical processes affecting it. Implicit-solvent models via the Poisson-Boltzmann (PB) equation provide insights with less computational cost than atomistic models, making large-system studies -- at the scale of viruses -- accessible to more researchers. Here we present a high-productivity and high-performance linear PB solver based on Exafmm, a fast multipole method library, and Bempp, a Galerkin boundary element method package. The workflow integrates an easy-to-use Python interface with optimized computational kernels, and can be run interactively via Jupyter notebooks, for faster prototyping. Our results show the capability of the software, confirm code correctness, and assess performance with between 8,000 and 2 million elements. Showcasing the power of this interactive computing platform, we study the conditioning of two variants of the boundary integral formulation with just a few lines of code. Mesh-refinement studies confirm convergence as $1/N$, for $N$ boundary elements, and a comparison with results from the trusted APBS code using various proteins shows agreement. Our binding energy calculations using 9 various complexes align with the results from using five other grid-based PB solvers. Performance results include timings, breakdowns, and computational complexity. Exafmm offers evaluation speeds of just a few seconds for tens of millions of points, and $\mathcal{O}(N)$ scaling. The trend observed in our performance comparison with APBS demonstrates the advantage of Bempp-Exafmm in applications involving larger structures or requiring higher accuracy. Computing the solvation free energy of a Zika virus, represented by 1.6 million atoms and 10 million boundary elements, took 80-min runtime on a single compute node (dual 20-core).
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