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  3. Generating Coupled Cluster Code For Modern Distributed-memory Tensor Software.
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  3. Generating Coupled Cluster Code For Modern Distributed-memory Tensor Software.

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Generating Coupled Cluster Code for Modern Distributed-Memory Tensor Software.

Jan Brandejs1, Johann Pototschnig1, Trond Saue1

  • 1Laboratoire de Chimie et Physique Quantique, UMR 5626 CNRS - Université de Toulouse, 118 route de Narbonne, Toulouse F-31062, France.

Journal of Chemical Theory and Computation
|July 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Developing efficient high-performance computing (HPC) software for coupled cluster (CC) calculations on GPUs is complex. This work introduces "tenpi," a framework for automated CC code generation, improving scalability and accessibility for complex molecular simulations.

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Area of Science:

  • Computational Chemistry
  • High-Performance Computing (HPC)
  • Quantum Chemistry

Background:

  • Efficient execution of coupled cluster (CC) computations on GPU-based High-Performance Computing (HPC) platforms is hindered by heterogeneous hardware structures.
  • Adapting software to these structures demands significant man-hours, necessitating systematized high-performance code development, especially for higher-order CC methods.

Purpose of the Study:

  • To address the challenges of efficient tensor symmetry capture and hardware abstraction in developing general-order coupled cluster (CC) code generators.
  • To present the design of a novel, open-source modular tensor framework, "tenpi", for CC code development.

Main Methods:

  • Development of a high-level problem representation translated into low-level hardware instructions via a compiler/translator.
  • Designing software to capture key tensor symmetries while maintaining hardware abstraction.
  • Integration of diagrammatic derivation, visualization, symbolic algebra, and intermediate optimization within the "tenpi" framework.

    • Main Results:

    • Systematically generated code demonstrates excellent weak scaling on up to 1200 GPUs using the ExaTENSOR distributed memory tensor library.
    • The "tenpi" framework supports multiple tensor backends and facilitates higher-order CC functionality.
    • Integration of "tenpi" into the DIRAC code's ExaCorr module enhances relativistic molecular calculations.

    Conclusions:

    • The developed general-order CC code generator and the "tenpi" framework significantly improve the efficiency and accessibility of CC computations on modern HPC platforms.
    • "tenpi" provides a robust, modular solution for advanced quantum chemistry calculations, enabling higher-order CC methods on massively parallel systems.