Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms

Raman spectroscopy offers invaluable insights into the chemical composition and structural characteristics of various materials, making it a powerful tool for structural analysis. However, accurate quantum mechanical simulations of Raman spectra for large systems, such as biological materials, have...

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Veröffentlicht in:SC24: International Conference for High Performance Computing, Networking, Storage and Analysis S. 1 - 12
Hauptverfasser: Shang, Honghui, Liu, Ying, Wu, Zhikun, Chen, Zhenchuan, Liu, Jinfeng, Shao, Meiyue, Li, Yingzhou, Kan, Bowen, Cui, Huimin, Feng, Xiaobing, Zhang, Yunquan, Truhlar, Donald G., An, Hong, He, Xiao, Yang, Jinlong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.11.2024
Schlagworte:
all-electron quantum perturbation simulation
Atoms
Biological system modeling
Biological systems
Computational modeling
heterogeneous architectures
High performance computing
Perturbation methods
Quantum computing
Quantum mechanics
Raman scattering
Raman spectra
scalability
Supercomputers
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Zusammenfassung:Raman spectroscopy offers invaluable insights into the chemical composition and structural characteristics of various materials, making it a powerful tool for structural analysis. However, accurate quantum mechanical simulations of Raman spectra for large systems, such as biological materials, have been limited due to immense computational costs and technical challenges. In this study, we developed efficient algorithms and optimized implementations on heterogeneous computing architectures to enable fast and highly scalable ab initio simulations of Raman spectra for large-scale biological systems with up to 100 million atoms. Our simulations have achieved nearly linear strong and weak scaling on two cutting-edge high-performance computing systems, with peak FP64 performances reaching 400 PFLOPS on 96,000 nodes of new Sunway supercomputer and 85 PFLOPS on 6,000 node of ORISE supercomputer. These advances provide promising prospects for extending quantum mechanical simulations to biological systems.
DOI:10.1109/SC41406.2024.00011