Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee 37996, USA.
School of Chemistry, University of Costa Rica, San José 11501-2060, Costa Rica.
J Chem Phys. 2023 Feb 28;158(8):084802. doi: 10.1063/5.0130797.
Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.
使用 MAGMA 线性代数库，在单个和多个图形处理单元 (GPU) 上加速密度泛函紧束缚 (DFTB) 方法。在我们的实现中，解决了 DFTB 基态计算的两个主要计算瓶颈：哈密顿矩阵对角化和密度矩阵构建。代码在两个不同的计算机系统上实现和基准测试：(1) 橡树岭国家实验室领导力计算设施的 SUMMIT IBM Power9 超级计算机，每个计算机节点具有 1-6 个 NVIDIA Volta V100 GPU；(2) 内部的 Intel Xeon 计算机，具有 1-2 个 NVIDIA Tesla P100 GPU。针对 1-、2-和 3-维化学系统的三个分子模型，即碳纳米管、共价有机骨架和水分子簇，测量了性能和并行可扩展性。
