A Unified Heterogeneous Implementation of Numerical Atomic Orbitals-Based Real-Time TDDFT within the ABACUS Package

TL;DR

This paper introduces a unified heterogeneous computing framework for real-time TDDFT using numerical atomic orbitals, significantly accelerating simulations on GPUs and demonstrating high scalability for large systems.

Contribution

It presents a co-designed abstraction layer approach in the ABACUS package, enabling efficient GPU acceleration and parallel scaling for RT-TDDFT calculations.

Findings

GPU acceleration yields substantial speedup over CPU implementations.

High parallel efficiency achieved across tens of GPUs.

Validated accuracy through optical property calculations for various systems.

Abstract

We present a unified heterogeneous computing framework for real-time time-dependent density functional theory (RT-TDDFT) based on numerical atomic orbitals (NAOs), implemented in the ABACUS package. We introduce three co-designed abstraction layers, including unified data containers, unified linear algebra operators, and unified grid integration interfaces. These layers collectively accelerate the two most demanding parts of NAO-based RT-TDDFT: explicit real-time wavefunction propagation and real-space grid operations such as Hamiltonian construction and force evaluation under external fields. We validate the method by computing optical properties for systems ranging from finite molecules to periodic solids, showing excellent agreement with standard benchmarks. Performance evaluations on bulk silicon demonstrate that a single GPU can achieve substantial wall-clock speedup over a fully utilized dual-socket CPU node. Furthermore, distributed multi-GPU strong-scaling tests confirm high parallel efficiency over tens of GPUs. This work establishes a high-performance, portable platform for large-scale first-principles simulations of ultrafast electron dynamics.