ArticleJournal of chemical theory and computation2021
Implementation of Relativistic Coupled Cluster Theory for Massively Parallel GPU-Accelerated Computing Architectures.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-GPU MBE(3)-OSV-MP2 for Performant Large-Scale Ab Initio Calculations.Journal of chemical theory and computation · 2026Article
- Acceleration of the Relativistic Dirac-Kohn-Sham Method with GPU: A Pre-Exascale Implementation of BERTHA and PyBERTHA.Journal of chemical theory and computation · 2025Article
- Accelerating Pythonic Coupled-Cluster Implementations: A Comparison Between CPUs and GPUs.Journal of chemical theory and computation · 2024Article
- Toward Pair Atomic Density Fitting for Correlation Energies with Benchmark Accuracy.Journal of chemical theory and computation · 2023Article
- Implementation of Relativistic Coupled Cluster Theory for Massively Parallel GPU-Accelerated Computing Architectures.Journal of chemical theory and computation · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In this paper, we report reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on the electronic structure are included from the outset. In the current work, we thereby focus on exact two-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as the starting point, but it is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program.
Identifiers
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Registered trials
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