Acceleration of Large-Scale Electronic Structure Simulations with Heterogeneous Parallel Computing

Large-scale electronic structure simulations coupled to an empirical modeling approach are critical as they present a robust way to predict various quantum phe-nomena in realistically sized nanoscale structures that are hard to be handled with density functional theory. For tight-binding (TB) simulations of electronic structures that normally involve multimillion atomic systems for a direct comparison to experimentally realizable nanoscale materials and devices, we show that graphical processing unit (GPU) devices help in saving computing costs in terms of time and energy consumption. With a short introduction of the major numerical method adopted for TB simulations of electronic structures, this work presents a detailed description for the strategies to drive performance enhancement with GPU devices against traditional clusters of multicore processors. While this work only uses TB electronic structure simulations for benchmark tests, it can be also utilized as a practical guideline to enhance performance of numerical operations that involve large-scale sparse matrices.