使用原子轨道的电子结构代码中线性和立方缩放轨道最小化方法的模块化实现
Modular implementation of the linear and cubic-scaling orbital minimization methods in electronic structure codes using atomic orbitals
AI总结：
本文在 SIESTA 中模块化实现轨道最小化方法，借助 DBCSR、ScaLAPACK、MatrixSwitch 与 libOMM 支持稀疏/稠密矩阵和大规模并行，并验证线性缩放求解器在数百原子绝缘体系中的性能优势。
摘要：
我们提出一种代码模块化方法，用于为采用原子轨道的电子结构计算设计高效且大规模并行的立方缩放和线性缩放求解器。轨道最小化方法的模块化实现通过外部库处理线性代数和并行化问题，并在 SIESTA 代码中进行了演示。DBCSR 和 ScaLAPACK 库分别用于稀疏矩阵和稠密矩阵的代数运算。最近在 Electronic Structure Library 中开发的 MatrixSwitch 和 libOMM 库有助于在不同矩阵格式之间切换，并实现能量最小化。我们展示了比较若干立方缩放算法性能的结果，同时演示了线性缩放求解器的并行性能，以及它们在含数百个原子的绝缘体系中相对于立方缩放求解器的优势。
英文摘要：
We present a code modularization approach to design efficient and massively parallel cubic and linear-scaling solvers for electronic structure calculations using atomic orbitals. The modular implementation of the orbital minimization method, in which linear algebra and parallelization issues are handled via external libraries, is demonstrated in the SIESTA code. The DBCSR and ScaLAPACK libraries are used for algebraic operations with sparse and dense matrices, respectively. The MatrixSwitch and libOMM libraries, recently developed within the Electronic Structure Library, facilitate switching between different matrix formats and implement the energy minimization. We show results comparing the performance of several cubic-scaling algorithms, and also demonstrate the parallel performance of the linear-scaling solvers, and their supremacy over the cubic-scaling solvers for insulating systems with sizes of several hundreds of atoms.
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