Scalable and Physics-Informed Multireference Implementation with Spin–Orbit Couplings via Modern HPC Clusters

In this Letter, we introduce a scalable and physics-informed (PI) computational implementation for determinant-based multireference (MR) calculations. The PI philosophy is deeply embedded throughout the methodology and its implementation. At the method level, the orbital entanglement is used to guide an optimized reconstruction of selected CI wave functions, while the MR stage employs a PI kernel optimization (PIKO) strategy that explicitly accounts for configuration grouping patterns and memory access irregularities of integrals. In system hardware codesign, the PI parallel optimization (PIPO) strategy is proposed, with which the most computationally intensive MR module is accelerated via an entropy-based performance model and a hierarchical load-balancing scheme; both of them exploit physical insights into workload irregularity and data locality. This consistent PI-driven approach enables remarkable heterogeneous computing efficiency and parallel scalability. Single-GPU accelerations reach ∼460 times those of full CPU cores, and the strong scaling efficiency exceeds 92.5% on up to 4000 GPUs using HPC clusters. Furthermore, scalar relativistic effects can be incorporated consistently, and spin-orbit coupling (SOC) effects can be included through the present SO treatment.