Massive-parallel Implementation of the Resolution-of-Identity Coupled-cluster Approaches in the Numeric Atom-centered Orbital Framework for Molecular Systems
Tonghao Shen, Igor Ying Zhang, Matthias Scheffler

TL;DR
This paper introduces a highly scalable parallel implementation of the RI-CCSD(T) method within the FHI-aims package, enabling efficient and accurate quantum chemical calculations for large molecular systems on supercomputers.
Contribution
It develops a domain-based distributed-memory algorithm for RI-CCSD(T) that minimizes communication and disk storage, achieving excellent scaling on thousands of cores.
Findings
Achieves strong scaling up to over 10,000 cores.
Demonstrates competitive performance with state-of-the-art HPC packages.
Shows negligible numerical error from RI approximation.
Abstract
We present a massive-parallel implementation of the resolution-of-identity (RI) coupled-cluster approach that includes single, double and perturbatively triple excitations, namely RI-CCSD(T), in the FHI-aims package for molecular systems. A domain-based distributed-memory algorithm in the MPI/OpenMP hybrid framework has been designed to effectively utilize the memory bandwidth and significantly minimize the interconnect communication, particularly for the tensor contraction in the evaluation of the particle-particle ladder term. Our implementation features a rigorous avoidance of the on-the-fly disk storage and an excellent strong scaling up to 10,000 and more cores. Taking a set of molecules with different sizes, we demonstrate that the parallel performance of our CCSD(T) code is competitive with the CC implementations in state-of-the-art high-performance computing (HPC) computational…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced NMR Techniques and Applications · Advanced Chemical Physics Studies · Machine Learning in Materials Science
