The tight-binding (TB) model is complementary to the ab-$initio$ methods that can be represented by the well-known Density Functional Theory since the empirical nature of a TB model enables the scope of electronic structure simulations to include realistically sized nanoscale structures that are normally made up of several million atoms. As the major computational hotspot of TB simulations comes from diagonalization of a sparse system matrix whose size is proportional to the number of atoms belonging to a simulation domain, efficient utilization of high performance computers is strongly encouraged. Here we study the feasibility of cost-efficient TB simulations for large-scale electronic structures on the Intel Xeon Phi Knights Landing (KNL) platform, which is equipped with the processor of many-integrated cores and the onboard high-speed memory. Using our in-house code whose scientific fidelity has been validated through various modeling researches, we present detailed discussion on how KNL systems can be exploited to accelerate simulations, and conduct rigorous benchmark tests in competing platforms to justify benefits of the standalone manycore architecture in terms of the time and the energy consumption that must be paid. The capability to handle extremely huge atomic structures in KNL systems is also demonstrated by securing a strong scalability up to 2,500 nodes in the NURION supercomputer for a model problem that has 400 million atoms.

紧束缚 (TB) 模型是对ab 的补充-$\mathrm{一世}n\mathrm{一世}吨\mathrm{一世}○$可以由众所周知的密度泛函理论表示的方法，因为 TB 模型的经验性质使电子结构模拟的范围能够包括通常由几百万个原子组成的实际尺寸的纳米级结构。由于 TB 模拟的主要计算热点来自稀疏系统矩阵的对角化，其大小与属于模拟域的原子数量成正比，因此强烈鼓励高效利用高性能计算机。在这里，我们研究了在英特尔至强融核 Knights Landing (KNL) 平台上对大规模电子结构进行经济高效的 TB 模拟的可行性，该平台配备多核处理器和板载高速内存。使用我们的内部代码，其科学保真度已通过各种建模研究得到验证，我们详细讨论了如何利用 KNL 系统来加速模拟，并在竞争平台中进行严格的基准测试，以证明独立多核架构的好处必须支付的时间和能源消耗。通过在 NURION 超级计算机中为具有 4 亿个原子的模型问题确保高达 2,500 个节点的强大可扩展性，也证明了在 KNL 系统中处理极其庞大的原子结构的能力。并在竞争平台中进行严格的基准测试，以证明独立众核架构在时间和必须支付的能耗方面的优势。通过在 NURION 超级计算机中为具有 4 亿个原子的模型问题确保高达 2,500 个节点的强大可扩展性，也证明了在 KNL 系统中处理极其庞大的原子结构的能力。并在竞争平台中进行严格的基准测试，以证明独立众核架构在时间和必须支付的能耗方面的优势。通过在 NURION 超级计算机中为具有 4 亿个原子的模型问题确保高达 2,500 个节点的强大可扩展性，也证明了在 KNL 系统中处理极其庞大的原子结构的能力。